Maximizing the utilization of the operating room suite by safely and efficiently changing over patients is an opportunity to deliver more value to patients and be more efficient in the operating suite. Lean Thinking is a concept that focuses on the waste inadvertently generated during organization and development of an activity, which should maximize customer value while minimizing waste. It has been widely applied to increase process efficiency and foster continuous improvement in healthcare and in the operating room environment. The objective of this paper is to provide insight on how healthcare professionals can be engaged in continuous improvement by embracing Lean Thinking and ultimately reducing changeover time between surgeries.Using an action research approach, Lean methodology such as Gemba walks, Process Mapping, Root-Cause-Analysis, and the Single Minute Exchange of Dies (SMED) system was applied to understand the causes of variability and wastes concerning changeovers and improve processes in the context of gynecological- and general surgery. Data were collected and analyzed through observations and video recordings. Problem and issue have been raised to management team attention and included in the annual balanced scorecard of the hospital. This initiative has been also made relevant to the team working in the operating suite and related processes before and after the entry of the patient in the operating suite.Improved patient flow and inter-professional collaboration through standardized and safer work enabled effective parallel processing and allowed the hospital to reduce changeover time between operations by 25% on average, without changes in terms of infrastructure, technology or resources.Lean thinking allowed the team to re-evaluate how the whole operating suite performs as a system, by starting from a sub-process as changeover. It is fundamental in order to improve further and obtain sustainable results over time, to act on a system level by defining a common goal between all stakeholders supported by a management and leading system such as visual/weekly management, optimizing planning, implementing standard-works to be followed by every associate and guaranteeing the role of the surgeon as process driver who pull performances. The continuous growth of healthcare costs is of major concern for the sustainability of public finances in the advanced Countries . HealthcThis issue has become even more important in the context of the coronavirus (COVID-19) pandemic, during which thousands of interventions were postponed as the Swiss Federal Council banned non-emergency interventions from March to April 2020, and hospitals have been confronted with the need to relocate resources to COVID-19 and intensive care units .Maximizing the utilization of the operating room (OR) suite by safely and efficiently changing over the rooms between surgeries is an opportunity to increase safety, quality and productivity while reducing patients’ waiting time for surgery and, as a result, increasing patients’ satisfaction .Many hospitals worldwide and in Switzerland have embraced the power of Lean Thinking . It can The OR suite functions as a “production site” of its own within a hospital, whose rules and processes differ from the rest of the organization . HealthcStandardization through the SMED methodology allows to improve the workflow of these major steps and to reduce the unnatural variability, hence the variability that is not dependent on the characteristics neither of the patients nor of the surgery, but on poor processes. Previous research has shown the positive impact of this methodology and operations management on changeover time between surgeries , 14–19. At the core of Lean Thinking lies the need for engagement from all stakeholders regarding the process to be improved at different levels of the organization. Process improvement initiatives, in order to be effective and sustainable over time, require the engagement and empowerment of the people closest to the work. Hence, the frontline HPs must be part of this process, as they have intimate knowledge of the work. They feel the ownership of their assigned tasks and can best determine which are the opportunities of improvement. Furthermore, these initiatives require administrative engagement and the support of the hospital executive board , 21. TheThe aim of this paper was to provide insight on how HPs can be engaged in continuous improvement by embracing Lean Thinking and ultimately reducing changeover time between surgeries.This paper presents an action research (AR) project which implements Lean Thinking and methodology on a macro-level and the SMED system on a micro-level in the context of gynecologic and general surgery in a public hospital located in Canton Ticino, Switzerland. The project focuses on changeover time between surgeries measured from last stich to incision.The primary end point of this project was the reduction of changeover times for gynecological and general surgery.This project was approved by the local Executive Board. Ethical committee was waived as data was fully anonymized and used for internal quality check only.worst-cases with changeover times of more than 1 h on average .The project was conducted at the Bellinzona e Valli Regional Hospital, Bellinzona, Switzerland, a hospital with 1,300 employees, and of these, more than 100 HPs work in the OR suit. The OR suite has five operating rooms, four induction rooms and one recovery room, performing about 7,000 interventions a year. Our institution recognized the gynecologic and general surgery to be This research approach was chosen as it is a collaborative and cyclical process – as Lean Thinking suggest – in which researchers and team members work closely together through five phases: (1) Diagnosis; (2) Action Planning; (3) Taking Action; (4) Evaluating; (5) Specifying learning , 24. ActEach phase of the process was concretized based on Lean Thinking: Gemba walks, Process Mapping and Root-Cause-Analysis were used both in the diagnosis phase and, in the evaluating phase, SMED was used as the key methodology to analyze changeover processes and define the actions and countermeasures to reduce changeover times. The design of the project and the respective methodology for each phase is reported in The project was conducted by an inter-professional team, led by the chair of anesthesia, and consisting of one nurse anesthetist, one surgical technician, one housekeeper, one OR administrative secretary, two surgeons and two head nurses representing the gynecology and general surgery specialties. The whole team was coached and supported by three internal Lean facilitators through each phase of the project. The first one was the Lean manager and researcher as part of the CAS in Healthcare Leader Excellence at the University of Bern. The second one was the head of the Information and Process management team, and the third one was the director of the hospital with his extensive Lean experience of more than 25 years of service in the industry.via workshops  over a total of 42 h, from April 2020 to January 2021, a time period that allowed the team to complete the Action Research cycle twice, and to test and adjust the countermeasures in the daily practice.The project was organized Gemba refers to “the real place where work is done”  performed in less than 10 min, hence a number of minutes expressed in a single digit, as achieved by Shingo. Although this time may not always be literally achievable, the system frequently leads to dramatic reductions in changeover times at low cost  as much as possible (action planning) and to streamline changeover workflow and standardize all the steps (action planning).Gemba walks were conducted over the course of 2 days with the goal of observing three elective surgeries for a total of two changeover times for gynecological and general surgery. A detailed process map is reported in The SMED analysis for the first changeover is reported in By separating internal steps (IED) from external steps (OED), the analysis showed that internal steps represented the majority of the changeover process, from last stich to incision. These steps included:1.Patient recovery and preparation for patient to leave the OR, performed by the anesthesiologist/nurse anesthetist and two surgical technicians.2.Patient leaving the OR (Patient OUT) with the anesthesiologist/nurse anesthetist.3.Tidying up surgical instruments  and OR cleaning (one-two housekeepers).4.Surgical instruments preparation and counting .5.Patient entering the OR (Patient IN) with the anesthesiologist/nurse anesthetist.6.Patient preparation and positioning .7.Skin disinfecting and draping .8.Time-out .9.Waiting times: patient waiting for induction; OR ready waiting for patient; OR team waiting for surgeons.A critical step was converting internal steps to external steps, and the following themes emerged.(1) Parallelize patient positioning and surgical instruments countPatient positioning was performed by one surgical technician and the nurse anesthetist, and lasted up to 20 min during which the rest of the team was waiting. Conducting patient positioning in the preparation room, when possible, would allow parallelization of this step with the OR cleaning and / or the surgical instruments preparation and counting. Barriers to this, identified by the team and then addressed as countermeasures, were the absence or passive role of the assistant surgeon / surgeon during patient positioning and a lack of standards concerning surgical positioning.(2) Convert surgical instruments preparation from internal steps to external stepsAs a countermeasure, the team could anticipate the preparation and counting of surgical instrument by preparing a sterile trolley in advance and by bringing them into the OR at the right moment. However, the current infrastructure does not feature a completely sterile area. This issue was identified by the team as an important element to be considered in new OR construction projects.(3) Patient recovery should always occur in the recovery roomAlthough this theme may be obvious, the recovery room has limited space and is used for small procedures as well. Thus, patients stayed in the OR longer.In terms of streamlining changeover workflow, the following themes emerged:1.Need to create clear and shared standard work at inter-professional level: lack of standards emerged as a key reason for limited performances. Specifically, concerning standard work for the OR cleaning process, standard work for surgical instruments preparation, standard work on how to prepare patient before  and after surgery, standard work on how to restore ORs after surgery, standard role for the assistant surgeon, standards to organize spaces. Inter-professional collaboration strategies were encouraged by implementing as much as possible the culture of feedback, social interactions, multidisciplinary problem solving and open communication.2.Importance of respecting OR planning and surgical cases scheduling: changes in the OR scheduling for the same day resulted in multiple phone calls within the OR and between the OR and the inpatient unit; furthermore, activities, materials, HPs and patients needed to be reorganized accordingly. Changes in the OR planning and scheduling impacts inevitably on the whole system.3.Key role of the surgeon as process owner: the presence of the surgeon in the OR from the beginning and his or her active role in the preparation process would increase the performance of the entire system. The surgeon’s presence means that the surgery can be performed. If during changeover the team knew they would have to wait for the surgeon regardless of how quickly they performed, the general quality of their performance suffered.4.Importance of improving changeover processes independently from surgery specialties: every OR has a dedicated team; however, as observed, changeover processes can occur between different specialties. Regardless of the surgery to be performed, changeover process times can and should be improved for all ORs independently from surgical cases, as the major steps are the same.While developing our action plan, goals were chosen to be clear, reachable, and with deadlines.Based on the diagnosis phase, the team developed two kind of action plans:quick wins, included general actions that would be implemented rapidly. For example, optimize nursing handover by reducing paper documents already available in the electronic patient system, or setting different times for the inpatient units to bring the first scheduled patient to avoid bottleneck and waiting times at the OR suite entrance. The quick wins actions emerged as countermeasures from the problems identified during the Gemba walks concerning the whole patient’s flow. The quick wins are available in The first action plan, The second and main action plan was developed based on the SMED analysis. For each countermeasure , key resBased on the insight from the inter-professional team, the lean facilitators created and mapped an ideal process as a reference model for the simulation in the OR and to train-the-trainers  on an inter-professional level. All new protocols were linked to the model as to define who has to do what and when. Times were defined based on non-complex surgeries. It can also be applied to every specific case . However, the defined protocols themselves do not change.A complete list of all the new procedures is available in the –Standard work for the OR cleaning process.–Standard work for surgical instruments preparation.–Standard work on how to prepare patient before and after surgery.–Standard work on how to restore ORs after surgery.–Role of the assistant surgeon as process owner.weekly management huddle. The latter is a short standing inter-professional meeting during which the OR inter-professional management team evaluate KPIs and define the necessary countermeasures on a weekly basis, at a fix defined day and time. Second, surgery departments designated a resident surgeon fully dedicated to the OR during the day.Actions and countermeasures to reduce changeover times were implemented from October to December 2020. Besides the countermeasures defined based on the SMED analysis, two further actions were undertaken. First, the changeover times were introduced as key performance indicators (KPI’s) at the OR weekly management showed a positive impact on changeovers times. However, 1 month after the implementation, changeover times still showed a great variability for both specialties . Therefore, the inter-professional team conducted a second diagnosis round to understand what could be improved further.The KPIs monitoring at the weekly management huddle.Gemba walks were conducted again over the course of 1 day, and two changeover processes between three interventions were video recorded and analyzed. Changeover times lasted 47 min and 67 min. This variability reflected the variability reported during the The following pitfalls emerged during this phase:(1) Absence of the key role of the surgeon as process ownerIn the first changeover, surgeons arrived when the patient was already positioned, and the patient needed to be re-positioned according to their indications. In the second changeover process, surgeons arrived on time, but did not contribute to the changeover process.(2) Late start of induction procedureIn the second changeover, the type of anesthesia defined required more time than 15 min to be given, and the anesthesia team did not coordinate effectively to induct the patient in time. Identified causes for the delay were the last-minute change of the kind of surgery for the same patient as well as the kind of anesthesia required.(3) Organization of the surgery inpatient unit: unit not ready to transfer the patientA time frame of 31 min passed from the OR phone call until the patient arrived in the OR. In this time frame, there were multiple phone calls between the OR and the inpatient unit to understand why the patient had not yet been transferred.(4) Changes in the or scheduling and planningThe OR scheduling was constantly changed, which consequently impacted the whole system and flows, including changeover processes. The particular context of the COVID-19 pandemic causes many changes because of the lack of bed available.(5) Low implementation maturity and compliance to the new standardsFinally, the countermeasures were evaluated and the team agreed that although standards were defined and written, not all employees followed them. The analysis revealed the need to improve the training of all employees, to sustain the changeover process and to implement and define new working standard.Historical data from 2018 to May 2021 showed a positive tendency of changeover times for both gynecological surgery and general surgery , 4. PerfChangeover times reached a new minimum . In comparison to 2019, the year before the current project, changeover times improved by 17 min on average for gynecology and 15 min for general. The primary objective of reducing changeover time by at least 25% was therefore achieved for both specialties.This paper has highlighted how HPs were able to improve inter-professional collaboration by creating a common and shared process, and ultimately reduce changeover time between surgeries by 17 min for gynecology and 15 min for general surgery, on average.The goal of the project was reached merely by improving inter-professional collaboration and enabling parallel processing. According to WHO, a “collaborative practice happens when multiple health workers from different professional backgrounds work together with patients, families, carers and communities to deliver the highest quality of care across settings” . HoweverImproved inter-professional collaboration in turn enabled an effective parallel processing. Parallel processing occurs when a separate room is used for the induction and often require additional staff . Unlike The key actions emerging from this project have also been highlighted by the FOPH , who conThe considerations explained above are fundamental when planning a similar project in another hospital or setting. Both the methodology of the project and the context are key elements. The project was an improvement initiative promoted by the hospital executive board and the OR suite management, hence frontline HPs, who together discussed and negotiated together the goals to improve performances and quality.weekly management huddle includes thus other relevant KPIs that define how the whole OR performance is measured on time start  admission delays (% of workdays with at least one delay in PACU admission).•Contribution margin per OR hour.•Turnover time .•Prediction bias (bias in case duration estimates per 8 h of OR time).•Prolonged changeover times (% of changeover that are more than 60 min).Macario  highlighThe implementation of Lean methodology allowed the hospital to reduce changeover time by 15 min and more for both specialties. Assuming an OR schedule for general surgery or gynecology with three surgeries (thus two changeovers), this would mean 30 min of time saved per room per day. Each OR minute is estimated to be worth 22–80 USD, as reported by Soliman et al. . SpecifiHowever, besides money, it is important to consider to reinvest some of the time recovered on improved safety and quality. By reducing the amount of time spent on changeovers, HPs can spend more time providing higher quality of care to patients . ProcessAlthough the project focused on changeover time specifically, it opened a discussion on a broad range of topics that are critical when planning the OR of the future. Two themes in particular have emerged from these discussions. First, the analysis and the countermeasures demonstrated that different surgery specialties organize themselves independently. The project allowed to clearly define the role of the surgeons and when they are expected to be in the OR. This is fundamental to optimize changeover time and provides greater motivation for all OR staff . The gynStrong leadership and management commitment, proven methodology and system thinking approach are keys to improve inter-professional collaboration and reduce changeover time between surgeries at low cost. The AR project allowed the hospital to reach the goal of reducing changeover times for gynecological and general surgery at least by 25% on average, without changing infrastructure, technology or other resources. For other healthcare facilities that wish to make changeover more efficient and obtain sustainable results over time, of most importance is to define a common goal between all stakeholders, define and implement standard work that regulate roles, responsibilities and tasks of all stakeholders involved in the process, define and execute parallel processing, by converting changeover steps from internal to external, introduce and/or reinforce the role of the surgeon as process owner, optimize OR planning and scheduling. Finally, the support of the hospital executive board is fundamental for the successful, sustainable implementation of changes and continuous improvement over time.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.AS, MA, AB, AV, and DL: protocol and project development. AV, CT, AL, MA, and FM: data acquisition and interpretation. FM: statistical analysis. MA, AV, CT, and AL: manuscript drafting. AS, FM, AB, AV, and DL: manuscript revision and accountable for all aspects of the work. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pesquisa Nacional de Saúde -PNS).to describe the access to and utilization of health services among theBrazilian population according to sociodemographic characteristics, based onthe 2019 National Health Survey  and were less likely to seekcare (17.6%); among those living in the North region, 69.1% had medicalconsultations; 16.5% of individuals with low level of education obtainedmedication through the Brazilian Popular Pharmacy Program.the results reinforce iniquities in access to and utilization of healthservices, in addition to the need for monitoring indicators in order toguide health policies in Brazil. Sistema Únicode Saúde -SUS) range from actions aimed at preventing diseases andhealth problems to diagnosis, treatment and rehabilitation of affected people, inaddition to health promotion and maintenance. In Brazil, universal and free accessto these services is a right guaranteed by the 1988 Federal Constitution and by theSUS, which was created in 1990. However, access to those services depends on both supply and availability andon the perception and needs of the individual and community.The services offered by the Brazilian National Health System ; among these changes, it is worthhighlighting the new Brazilian National Primary Healthcare Policy, in 2017, and thePrevent Brazil Program, in 2019, which reformulated the primary health carefinancing policy. Since then, as a result of the new health policies, the estimate of PHCfunding within the SUS has been based on the number of citizens registered in themunicipalities and the performance achieved by health teams, the latter evaluatedthrough indicators and goals defined by the Ministry of Health. In addition, newpossibilities of relationship between the Government and private companies have comeinto force, in such a way that public and private sectors can participate,indistinctly, in the provision of health services within the SUS, resulting in thereduction of the state's duty, defined constitutionally, to promote health care forthe Brazilian population. Thus, the expansion in access and service quality tends tobe reduced, given that possible funding distortion may restrict the performance of PHC. In this sense, identifying the gaps in access to and utilization of publichealth services in this new scenario can contribute to identifying needs andreducing iniquities in universal access to services.National and international studies have emphasized that the increase in the supply ofhealth services is not enough to ensure greater access and use of these services,especially among the most vulnerable populations.Pesquisa Nacional deSaúde - PNS).The aim of this study was to describe the access to and utilization of healthservices by the Brazilian population according to sociodemographic characteristics,based on data from the 2019 National Health Survey (Instituto Brasileiro de Geografia e Estatística-IBGE) in partnership with the Ministry of Health, were analyzed. Thissurvey aims to produce data on the health situation and lifestyle of the Brazilianpopulation, as well as on health care at the national level, with regard to accessand utilization of services, preventive actions, continuity of care and health carefinancing.This was a descriptive cross-sectional study, in which data from the 2019 PNS, apopulation-based household survey conducted by the Brazilian Institute of Geographyand Statistics  were stratified by municipalities,through simple random selection. The sample size was defined in 8,036 PSUs, whichrepresent 53% of the PSUs and correspond to a set of area units from which it ispossible to select subsamples that meet the survey objective. In the second stage,the distribution of households by PSU were carried out, where the interviews wouldbe conducted: for Federative Units (FUs) with the largest number of PSUs, thesmallest number of households (12 households); for FUs with the smallest number ofPSUs, the largest number of households (18 households); and finally, for FUs thathad not been included in the two previously defined criteria, the number establishedwas 15 households per UPA. In the third stage, a resident aged 15 years and olderwas selected, with equiprobability among all adult residents of the household, toanswer the individual interview. In this study, the additional eligibility criterionwas to have answered questions about access to and utilization of SUS and privatehealth services, regardless of sex and place of residence.More detailed descriptions of the sampling process and data collection methods can befound in the 2019 PNS report, available in the IBGE website. Data were accessed onJuly 21, 2021.PNS data collection took place between August 2019 and March 2020, by means of theproposition of a questionnaire comprised of three blocks of information: a)household variables; b) general characteristics of all residents of the household;and c) questions about work and health, directed to a randomly selected resident.The questionnaire was applied by properly trained interviewers, according to thepersonal digital assistant (PDA). The interviews were scheduled according to theavailability of each resident, and two or more attempts to visit each household wereestablished.The studied variables that were related to the utilization of services, referred tothe following questions:Stopped performing usual activities for health reasons in the last twoweeks;Sought medical care from the same medical professional or sought for the samehealth service in the last two weeks;Had a doctor's appointment in the last 12 months;Had a dentist appointment in the last 12 months;Sought medical care in health services in the last two weeks;Got medical care the first time you sought it, in the last two weeks;Got medical care and prescriptions filled, in the last two weeks;Obtained all prescribed medicines (by some means);Obtained at least one of the medicines prescribed in the last health carevisit;Obtained at least one of the medicines prescribed through the BrazilianPopular Pharmacy Program (Programa Farmácia Popular - PFP) in the lasthealth care visit;Obtained at least one medicine through the public service, in the last healthcare visit;Have been hospitalized for 24 hours in the last 12 months; andLast hospitalization in a SUS unit in the last 12 months.All questions used were dichotomized: no; yes.The prevalence of the variables and respective 95% confidence intervals (95%CI) werecalculated through stratification by sex , age groups , level of education/educationof the head of the family  and macro-region of thecountry .Data analysis was performed using the survey module of the Stata software version16.1, which takes into account complex sampling design effects.Comissão Nacional de Ética em Pesquisa -CONEP)/National Health Council and approved: Opinion No. 3,529,376, issued on August 23, 2019. All respondents wereconsulted, duly informed and agreed to take part in the survey.The project of the 2019 National Health Survey was submitted to the National ResearchEthics Committee  who stopped performing usual activities for health reasons,in the two weeks prior to the survey. Males showed a lower proportion of medicalconsultations in the last 12 months , a lower number ofdental visits  and they also were less likely to seek healthcare in the two weeks prior to the study . The otherindicators presented similar proportions between the sexes, as shown in As for schooling, an inversely proportional relationship was found, with a higherprevalence among those with low level of education, when taking into considerationthe following indicators: stopped performing usual activities for health reasons inthe two weeks prior to the survey ; had a health care visitin the last two weeks and obtained prescribed medication ;obtained, through the Brazilian Popular Pharmacy Program, at least one of themedicines prescribed in the last health care visit ; andobtained, through the public health service, at least one of the medicinesprescribed in the last health care visit . However, theparticipants with high level of education showed a higher proportion of dentalvisits in the last 12 months  and also a higher proportionof obtaining all the medicines prescribed in the last health care visit , when compared to participants without schooling or low level ofeducation .The analysis of the main results of this study suggests inequalities in the access toand utilization of health services in Brazil. The findings showed a lower use ofdental services among older adults, and for those who lived in the North andNortheast regions and among those with low level of education, additionally, a lowerproportion of health care visits aimed at seeking for medicines through theBrazilian Popular Pharmacy Program or public service, highlighting the differencesin this access among sociodemographic groups. Paradoxically, the proportional reduction of those who got medical care thefirst time they sought it, from 95.3% in 2013 to 67.9% in 2019, is noteworthy. This result represents a setback in the advances that the Brazilian NationalHealth System has achieved over the last 30 years, especially in primary healthcare, a possible reflection of the government initiatives that have been adoptedsince 2017, and caused a drastic reduction in funding, decrease in health servicesand human resources, leading to the weakening of services and health care for theSUS users.When comparing the results presented with data from the 2013 PNS, a small increase inaccess to medical and dental consultations was identified in the last 12 months, aswell as in seeking care and the care provided in the last two weeks prior to thesurvey, a result also identified in other national studies. Moreover, the reduction in access to medicines mainly exposes older adultsand chronically ill people, who are the biggest users of these programs, a situationthat is likely to worsen as the demographic and epidemiological transition advances.In this sense, access to medicines through the Brazilian Popular Pharmacy Program andpublic services was reduced by about 15%, between 2013 and 2019. For 15 years, PFPhas been expanding and promoting the population's access to medicines, especiallyamong older adults and people with chronic non-communicable diseases (NCDs),therefore it received recognition from the World Health Organization (WHO), giventhe results achieved. Men, in addition to presenting a higher prevalence of health risk behaviors,such as smoking, alcohol consumption and sedentary lifestyle, are also less likelyto seek health care, which increases the chances of long-term problems. Accordingly, Bibiano et al. showed, in their study, that the care of elderlymen has a curative and rehabilitation profile, rather than prevention and healthpromotion.Regarding sex, men have presented a lower access and use of health services, a factthat is widely discussed in the national and international literature.The highest frequency of seeking medical care and the lowest immediate access todental care were mostly identified among older adults. The physiological process ofaging, together with the highest occurrence of chronic diseases in this population,in addition to the promotion of public policies aimed at increasing and facilitatingaccess to services, makes older adults seek more health care, a demand that has beenincreasing in recent years, with an upward perspective, even though the difficultyof access in some areas reinforces an important gap in the supply of health services.PolíticaNacional de Atenção Básica – PNAB) and conduction of otherpopulation-based surveys, such as the Chronic Disease Risk and Protective FactorsSurveillance Telephone Survey (Sistema de Vigilância de Fatores de Risco eProteção para Doenças Crônicas por Inquérito Telefônico - VIGITEL). The lowest access to medical and dental consultation in the North andNortheast regions is associated with the lowest availability of these professionalsin the public network, because, although in recent years, policies to expand healthservices in Brazil have focused on the most vulnerable regions and reducedinequalities, the guarantee of human resources to provide care for the population isstill difficult.Furthermore, regarding the macro-region of residence, the results of this studyportray the regional inequalities in the access and use of health services inBrazil, a fact recognized by the Ministry of Health through initiatives such as theestablishment of the Brazilian National Primary Health Care Policy ( barriers such as low income, low level of education and poor supply of publicoral health services are still obstacles to meet the existing demands, especiallyfor older adults. The results of this study corroborate the literature by showing that theavailability of oral health care is still limited for the most vulnerable people. Itis worth mentioning that 70% of the Brazilian population relies on the SUS as theironly source of access to health services, in addition to the fact that the number ofdental services distributed over the national territory is insufficient to providecare for the general population, causing repressed demand and difficulty of accessto services.Stratification by level of education reinforces inequalities in access to healthservices. Although trend studies have shown increased access to dental care in Brazil, Thus, it can be inferred that those with lower income or lower socioeconomicstatus have greater difficulty and dependence on the SUS in order to access to medicines. The study also showed that people with low level of education obtained,through the Brazilian Popular Pharmacy Program, at least one of the medicinesprescribed in the last health care visit, and they obtained through the publichealth service at least one of the medicines prescribed in the last health carevisit. These results reinforce the principle of equity promoted by the SUS in accessto medicines, overcoming disparities between social groups within this healthservice.Socioeconomic status also appears as a factor related to access to and utilization ofdifferent health services: higher income or better socioeconomic status maintains adirect relationship with a higher probability of using health services, such asaccess to and use of medicines.This study presents as a limitation the method used to obtain the information fromthe PNS, extrapolated here for the population, although it was provided by a singleindividual, as representative of the other household members, in addition toadopting the level of education in the stratification of the population by incomeand not taking into consideration the per capita income. However, the study was ableto analyze important health characteristics of the Brazilian population, by takinginto account that, despite the improvement observed in some indicators, such as ahigher number of medical and dental consultations, the SUS – as the main and often,the only way to access health services for the low-income population – is stillinsufficient, since great regional inequality persists: the North and Northeastregions have the least access to services, and individuals with low socioeconomicstatus are those who face more difficulties in having their demands met.The results presented indicate the relevance of monitoring the sociodemographiccharacteristics and vulnerabilities of the population. Knowledge of these conditionsallows us to understand their influence and thus contribute to a more effectiveperformance of the Brazilian National Health System in order to guide equitablehealth policies, aimed at identifying risks and population demands for access to andutilization of health services in Brazil.


The aim of the study was to investigate and implement modern surveying techniques to improve usage of reservoir storage during hydropower plant drawdown operations. The experimental results show that thresholding of the NGRDI and supervised classification of the NDWI were the best-performing methods for the shoreline detection in the fish spawning sites.This paper presents research concerning dewatered areas in the littoral zones of the Kaunas hydropower plant (HPP) reservoir in Lithuania. It is a multipurpose reservoir that is primarily used by two large hydropower plants for power generation. As a result of the peaking operation regime of the Kaunas HPP, the large quantity of water that is subtracted and released into the reservoir by the Kruonis pumped storage hydropower plant (PSP), and the reservoir morphology, i.e., the shallow, gently sloping littoral zone, significant dewatered areas can appear during drawdown operations. This is especially dangerous during the fish spawning period. Therefore, reservoir operation rules are in force that limit the operation of HPPs and secure other reservoir stakeholder needs. There is a lack of knowledge concerning fish spawning locations, how they change, and what areas are dewatered at different stages of HPP operation. This knowledge is crucial for decision-making and efficient reservoir storage management in order to simultaneously increase power generation and protect the environment. Current assessments of the spawning sites are mostly based on studies that were carried out in the 1990s. Surveying fish spawning sites is typically a difficult task that is usually carried out by performing manual bathymetric measurements due to the limitations of sonar in such conditions. A detailed survey of a small (approximately 5 ha) area containing several potential spawning sites was carried out using Unmanned Aerial Vehicles (UAV) equipped with multispectral and conventional RGB cameras. The captured images were processed using photogrammetry and analyzed using various techniques, including machine learning. In order to highlight water and track changes, various indices were calculated and assessed, such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Visible Atmospherically Resistant Index (VARI), and Normalized Green-Red Difference Index (NGRDI). High-resolution multispectral images were used to analyze the spectral footprint of aquatic macrophytes, and the possibility of using the results of this study to identify and map potential spawning sites over the entire reservoir (approximately 63.5 km In a previous study, water level (WL) fluctuations in the Kaunas hydropower plant (HPP) reservoir were analyzed . It was The Kaunas HPP reservoir is situated on the East European Plain. The littoral zone (the nearshore area) of the reservoir is generally shallow with gentle slopes. It is a multipurpose reservoir, which is mainly used for power generation by the Kaunas hydropower plant and Kruonis pumped storage hydropower plant (PSP); however, it is also used for recreation, navigation, irrigation, industrial water supply, flood management, and recreational fishing . FrequenThe littoral zone provides a spawning habitat for fish and water birds . It is aThe most abundant fish species in the Kaunas HPP reservoir belong to the phytophilic ecological group of fishes. It was determined that the main fish species whose spawning conditions may be affected by the fluctuating water level in the Kaunas HPP reservoir are pike, common roach, perch, and bream . They usThe main focus of this study was to implement remote sensing (RS) techniques to track changes in drawdown areas in potential fish spawning sites. With the fast development of RS technologies, software, and the popularity of drones, surveying difficult areas has become easier and more accessible. Surveying with drones is a cost-effective and time-efficient method to collect data from the air, providing imagery or point cloud data from which a variety of deliverables can be extracted. According to various sources, data can generally be captured five times faster with drones than with traditional land-based methods ,22. For Studying reservoir drawdown areas is a complex task, and several different approaches can be implemented. Different drone surveying methods for high-resolution river landscape mapping of the Belá River in the northern part of Slovakia were discussed in . Three iAfter data acquisition, processing, and validation, the resulting orthomosaic images can be used to detect the reservoir shorelines. A comparison of the shorelines detected at different stages of the HPP operation indicate the dewatered areas. There are many studies using various remote sensing techniques for shoreline detection. Moreover, various techniques have been used for different RS data sources. Satellite images , data collected by Light Detection and Ranging (LiDAR), and aerial photography from the planes or UAVs were used in previous studies, some of which are discussed in Spectral reflections from different surfaces are the key to the techniques used in this study. In 1996, McFeeters introduced the Normalized Difference Water Index (NDWI), which makes use of reflected near-infrared (NIR) and visible green light radiation for assessing quantity  and quality  of water resources . Hanqiu Part of the study was carried out with a drone equipped with a simple RGB camera. Data of NIR were unavailable for these datasets; the NDVI and NDWI could not be calculated. Different combinations of red, green, and blue bands were used to separate water, aquatic, and land vegetation. The purpose was to establish the best-performing technique as compared to methods that rely on NIR. In this way, the technique could be adopted for datasets that only contain red, green, and blue bands, which correspond to the visible part of the electromagnetic spectrum and can be captured with a simple camera sensor. The Visible Atmospherically Resistant Index (VARI) and the Normalized Green–Red Difference Index (NGRDI) indices using only the visible range of the spectrum were calculated. The VARI was proposed by  for remoThis research differs from others in the following ways: We are interested in changes in the submerged area in potential fish spawning sites, which means that the shoreline must be detected in areas containing aquatic macrophytes. In areas that are clear of aquatic vegetation, many known techniques, as discussed above, can be implemented; however, in the areas with thick water vegetation, it is very difficult to detect and segment water. Therefore, the best methods and indices to achieve this were evaluated.A spectral signature analysis was carried out for aquatic and land vegetation in order to separate one from the other. This means that potential fish spawning sites could be detected over the entire reservoir and their area could be calculated. This knowledge would allow us to locate the most vulnerable areas and estimate how much water level fluctuations affect them. A similar study of spectral signatures was conducted for automatic blackgrass weed mapping using a supervised classification technique . A numbeThe aim of the study was to implement modern remote sensing techniques to investigate dewatering areas in the fish spawning sites. Current assessments of spawning sites in the reservoir are mostly based on studies that were carried out in the 1990s. Surveying fish spawning sites is typically a difficult task that is usually carried out by performing manual bathymetric measurements due to the limitations of sonar. Majority of the devices based on echo-sounding (sonar) cannot operate in the shallow waters containing aquatic macrophytes—the typical spawning sites for many species of fish. Our hypothesis is that RS can be used to assist in surveying difficult and vulnerable areas, where the current and accurate data are needed. This knowledge could assist in making decisions for better use of reservoir storage while increasing power generation.To review current practice related to reservoir drawdown/hydropower operating rules and policies;To conduct surveys of the study area using remote sensing methods;To detect shorelines during different stages of reservoir operation;To evaluate the effectiveness of different methods of shoreline detection in various nearshore conditions;To analyze the spectral signatures of aquatic macrophytes and to evaluate the possibility of implementing the results for automatic classification over the entire Kaunas HPP reservoir;To summarize the findings in order to provide insights for deriving the optimal operating rules for the reservoir.Various tasks were set for this study:2 and the volume is 0.46 km3. The effective capacity of the reservoir available for hydropower is 0.22 km3. The average depth of the Kaunas HPP reservoir is 7.3 m, and the depth of the reservoir is 10–12 m in the lower part and about 4–5 m in the upper part [The Kaunas HPP reservoir is the largest artificial water body in Lithuania. The reservoir was created by damming the Nemunas River in 1959. The reservoir is used by two large hydropower plants—the Kaunas Hydropower plant, which has an installed capacity of 101 MW and a rated head of 20.1 m, and the Kruonis Pumped Storage Hydropower Plant (PSP), which currently has an installed capacity of 900 MW from four turbines and a rated head of 93.6–111.5 m (depending on the water level in the upper reservoir). The Kaunas HPP reservoir is mainly used for power generation but is also used for recreation, navigation, irrigation, industrial water supply, flood management, and recreational fishing. Water level fluctuations in the Kaunas HPP reservoir are mostly dependent on power plant operation and the upstream inflow from the Nemunas River. The impounded area of the Kaunas HPP reservoir at a normal water level (NWL) is 63.5 kmper part .Operating two large HPPs causes water level fluctuations in the reservoir. The reservoir operation rules were established to protect the environment and ensure other stakeholders’ needs. There are two main operation regimes. During normal operation, the permitted water levels in the reservoir are between 43.5 and 44.4 m a.s.l. A daily water level drawdown of ±0.4 m  is allowed. During the fish spawning period, the operation of the Kaunas HPP and Kruonis PSP is restricted. The fish spawning period occurs between 1 April and 30 June. During the restricted regime, the headwater elevation of the Kaunas HPP reservoir must be between 43.7 and 44.0 m a.s.l. A maximum difference of 10 cm between the highest and the lowest daily water level  is allowed. A daily water level change of 20 cm is allowed in the reservoir if, each year, the operator commissions an environmental study according to the Environmental research program and compensates for the environmental damage if it was caused [The northeastern shore of the Kaunas HPP reservoir was selected as the study area. It is one of the main spawning sites for perch, common roach, and common bream , and is Planned drone flights were carried out throughout the summer and autumn of 2021 during different water level fluctuations in the reservoir. There were two drones used for the study, i.e., DJI Martice 200 equipped with the 12 MP Sentera AGX710 multispectral camera and Autel EVO II Pro equipped with the conventional XT705 20 MP RGB camera. For each flight, the same mission was carried out in the same area (approximately 5 ha). The survey area, flight paths, and settings were prerecorded and used for each mission. During the mission, the drones took approximately 350 images of the area from a 60 m elevation with an 80% front and 75% side overlap. Drone flight mission information is presented in In order to consistently process the images with a high accuracy, 10 permanent ground control points (GCP) were established. The photogrammetric process needs the support of control points to be able to scale and to georeference the model . Most ofCoordinates of those points were measured with the Trimble R6 GNSS receiver. GCPs were visible in the photos that were taken and referenced to the exact coordinates with the photogrammetry software. Water levels were measured at the start and end of each mission using a water level gauge, which was leveled with the Trimble R6 GNSS receiver to determine the accurate water level altitude during each mission.Data from UAVs were processed using photogrammetry programs, i.e., Agisoft Metashape Pro and Pix4Dmapper. Drone surveying and mapping is commonly used in civil engineering, agriculture, land surveying, and other fields. The workflow of drone surveying and data processing with different applications is discussed in numerous articles, some of which ,22,23,24The techniques discussed in the articles and software documentation  were useVarious indices were calculated using the spectral bands of the orthomosaics in order to separate water from other areas . Indices were calculated to simplify complex data amalgamations, e.g., orthomosaic images. The NDWI and NDVI were used to highlight the water from other surfaces during the analysis of multispectral images. These indices were derived from the NIR, Green (G) and Red (R) spectral bands. They are dimensionless indices that describe the difference between visible and near-infrared reflectance of surfaces. The orthomosaics created from RGB images did not have an NIR band available. For this reason, the VARI and NGRDI indices were used in the same application to highlight the pixels that represented water. Equations of the aforementioned indices can be found in the literature that was reviewed in The resulting raster images of various indices were thoroughly analyzed. The index values of four different surface conditions in the study area were compared. The thresholding technique was used to segment images into several classes—water, aquatic macrophytes, land vegetation, and sand. Generally, a threshold limits can be selected by trial and error  or using the peak–valley method of histogram segmentation . In thisTwo different areas were analyzed to evaluate the performance of different approaches. The first area was a wavy shoreline with steep banks. The water in that area was mostly free from aquatic vegetation, so the shoreline, between land vegetation that covers the banks and the water, was well-defined. The more challenging part was between the bends of the shore, which were shaded from the sun. The second area was the opposite—the banks were very shallow, covered with sand, and clear from vegetation. One part of the sandy beach was covered with silt. Thresholding and unsupervised classification were carried out for the raster images containing NGRDI, VARI, NDVI, and NDWI values.In the spawning sites, shoreline detection was complicated because the aquatic vegetation obstructed the water. For this reason, two different image classification techniques were used to obtain a simplified data representation of a set of homogeneous and natural regions  to help detect the shorelines. Unsupervised classification was carried out for all the raster images. The outcome was groups of pixels with common characteristics  based on the software analysis of an image without the user providing sample classes. This task was carried out with ArcGIS PRO software using ISODATA (ISO) clustering. Unsupervised  classification methods are explained in . HierarcSupervised classification was mainly used in an attempt to improve the results in the areas with dense aquatic vegetation. For this step, orthomosaics and raster images of the NDWI and NGRDI values were analyzed. These indices were chosen as they performed the best for multispectral and RGB image segmentation. Supervised classification was carried out by defining sample areas, otherwise known as Regions of Interest (ROI). The defined areas were analyzed using the supervised machine learning algorithm (Random Forest) to distinguish the characteristics of the provided areas. The RF ensemble classification and regression approach was developed by . It exhiVarious water body segmentation techniques by satellite image classification were discussed in ,40,41. TRaster images containing NDVI and VARI indices were not segmented using supervised classification as these indices did not improve the results in the first part of the study . The resA DJI Matrice 200 equipped with multispectral camera was used on 8 June 2021. The water level in the reservoir during the flight was 43.76 m a.s.l. The multispectral camera captured images with six bands (wavelengths range was 400–900 nm): red, blue, green, red edge 1&2, and near-infrared. These data allowed for a deeper analysis of the area. The main purpose was to study the spectral footprint of the aquatic macrophytes and land vegetation. Spectral reflections of four different surfaces were analyzed: water, land vegetation, water vegetation, and bare sand. Areas that represented studied areas were carefully selected and their spectral signatures were calculated using QGIS. The Bray–Curtis similarity index was calculated to estimate if the surfaces could be separated .The different shoreline detection techniques exhibited different strengths and shortcomings when appd in different conditions. A comparison of the resulting shorelines is presented in The first set of pictures was the orthomosaics of the two selected locations (a and b). The shorelines were detected by visual inspection from high-resolution orthomosaic images and are shown as the red line. This remains in all the other pictures as a reference. The second set of pictures is the classified NGRDI pictures using unsupervised classification. The algorithm detected five different classes, which is the same outcome as that of the VARI raster image classification. Overall, the outcome of the NGRDI and VARI unsupervised classification was essentially the same, and NGRDI was chosen for further investigations as it is easily readable, where values range from −1 to 1. The outcome of the classified NGRDI/VARI raster images was the worst. There was a great deal of noise (incorrectly classified pixels) in the water and land areas. In the first location (a), the shoreline was the least defined, and in the second location (b), the algorithm was unable to separate water from the wet sand on the shore. The third set of the pictures is classified images of NDVI and NDWI values. In both cases, the unsupervised classification algorithm (ISO clustering) detected seven classes and both outcomes were very similar. The NDWI represented water with a little less noise as it is designed to do, but overall, in this case, the two techniques were essentially interchangeable. The use of the NIR band helped to separate water from the vegetation. In the first location, the shoreline was best defined and the shadows did not have an impact on the outcome. However, in the second location, this method was unable to separate the water from the sand. Bare wet sand absorbs NIR in a similar manner to water. In both cases, the difference in the indexed values was not enough to separate wet sand from water. The fourth set of pictures was classified orthomosaic. The algorithm detected five different classes. Unlike in the other cases, it separated a class for shadows (dark areas). The noise in the classified pictures was average. The algorithm was considering the entire light reflectance spectrum captured by the camera and was able to separate water from sand, assigning it a different class. However, this was not done accurately, i.e., the area with silt on the sand was assigned to the water class and the shallow part of the reservoir with sand on the bottom was assigned to sand. The resulting shoreline in the shallow part of the reservoir was not accurate.The assessment of accuracy and quality of unsupervised image classification was carried out to determine the best-suiting indices for highlighting water. The results of accuracy evaluation are provided in The accuracy assessment showcased that the best index for water identification was NDWI, which was to be expected. NGRDI showed to be a valuable option of water and vegetation identification if NIR is not available. It has performed well at OA of 92% and KC 0.837, which is according to categorization of the Kappa statistic—almost perfect . These iThe accuracy assessment of the classification of the forementioned surfaces was done by careful visual inspection. To improve the classification accuracy, the thresholding technique was applied for the NGRDI, NDWI, and NDVI raster images. This technique allowed us to assign an interval of index values to different classes. This technique allowed us to fine-tune the results, to study the index values of certain areas, and to assign their range to a different class. Thresholding the NDVI and NDWI allowed us to separate sand, but the part with the silt on the sand had no distinguishable difference from the water. Thresholding the NGRDI was promising. The index is calculated from the reflections of the wavelengths in the red and green spectrum and was able to separate sand from water relatively well , the transitional area that is constantly affected by the waves (wet sand) was noisy, with some pixels being classified as water and others being classified as sand. The area with wet sand is marked between the two red lines. NGRDI thresholding is more useful in this scenario because the water, and wet and dry sand have some separation from each other, according to the NGRDI values. In the case of steep banks, where the shoreline can be defined as the separation between water and land vegetation (b), the supervised classification worked very well and the shoreline was defined very accurately. The resulting image of NGRDI thresholding has more noise, which would make automatic shoreline detection difficult without filtering. Overall, manually defining different classes for the machine learning algorithm improved the results and classified images had less noise. The separation of water from the sand was more pronounced and the algorithm was able recognize more surfaces. The resulting images were more detailed, but it struggled in the same conditions as unsupervised classification, i.e., shallow sandy areas where improvement was just slight, and trying to define those areas with more input for machine learning algorithm resulted in more noise in the rest of the image (pixels that were assigned to wrong classes), which produced the opposite effect. Classification of the images with NGRDI values did not produce better results than thresholding. Thus, supervised classification is not always the best solution, and sometimes, depending on the conditions of the nearshore of the reservoir, NGRDI thresholding is a better approach. Studying NGRDI values of different surfaces can result in accurate image segmentation into different classes, and in the case of the shallow sandy areas it was the most accurate option.It is possible to detect shorelines in areas with medium-density aquatic macrophytes (bulrushes in this case). NGRDI thresholding was able to indicate water in such conditions with relative ease .Remote sensing techniques have great advantages over the traditional methods in these areas because the vegetation does not obstruct the water, and shorelines can be detected easily. Traditional bathymetric surveys in such areas are difficult. Echo-sounding is not possible due to vegetation, and measuring points with an RTK-GPS receiver is physically demanding. Areas in which traditional methods have a clear advantage are those with dense aquatic vegetation .When the density of aquatic vegetation is high , it is very difficult to detect the shoreline using RS techniques. Dense aquatic macrophytes almost completely obstruct the water. There were not many pixels indexed as water and judging the location of the shoreline was very difficult. The thresholding of NGRDI allowed for better separation of the surfaces, but in these cases, the shoreline could only be approximated.The shorelines derived from bathymetric measurements (according to the generated DEM) and RS were compared .It is noticeable that the shorelines derived from RS were more accurate in areas with an absence of or medium-density aquatic vegetation. The shorelines derived from the bathymetric measurements were not as detailed because the digital elevation model was interpolated between the measurement points. Generally, the results derived from traditional land-based surveys will never be as detailed because it is not possible to measure enough points to match the spatial resolution of the data collected by RS. Although bathymetric measurements in areas with dense aquatic vegetation are difficult, but it was possible to obtain accurate bathymetric data of the reservoir, which is where it has an advantage over remote sensing. The shorelines determined using the morphological method might not have been as detailed, but they were accurate enough to calculate dewatering areas in the fish spawning grounds at any given water level throughout the entire operating range of the HPPs, where the shoreline detection in such areas using RS is possible but complicated and might be not as accurate depending on the conditions.A spectral signature analysis was carried out for the orthomosaic image captured with the multispectral camera .Judging from the graph, sand produced the highest reflection of blue, green, and red. This is expected because it is a light color, i.e., it reflects the light the best and the values in the range of NIR are lower, because NIR is absorbed by the sand. Water reflects light in the blue and green spectrums the most. In the summertime, water in the Kaunas HPP reservoir has a light green tint and the NIR spectrum reflectance is lowest out of all of the analyzed areas, because water absorbs the NIR the most. The reflectance in the near-infrared region of the electromagnetic spectrum suggests that the water may have contained a small amount of algae. The most important part of this analysis was the comparison of land and water vegetation. It is visible in the diagram that overall water vegetation had a higher reflectance in the RGB range, with the largest difference being noticeable in the blue spectrum. Some yellow reeds made up the water macrophytes; for this reason, red and blue reflectance was higher as compared to the land vegetation. The NIR reflectance was quite similar for land and water vegetation, with the land vegetation reflecting the NIR a little more, because it was denser in general. A statistical analysis of the spectral reflectance of land and water vegetation was performed for further investigation .The standard deviation of the reflection values in the visible region of the electromagnetic spectrum of the water vegetation class was higher because it comprised different types of aquatic macrophytes with different signatures. Analyzing the spectral signatures of the subclasses that were defined for land and water vegetation, the Bray–Curtis similarity index ranged from 71 to 89%.2 spatial resolution with a precision of ±15 cm for remotely sensed datasets [According to our previous study on dewatering areas during drawdown operations and water level fluctuations in the reservoir , we demodatasets . In the datasets . The datdatasets  but it ddatasets , and thedatasets . An article by  presentsAnother approach involves the use of airborne Light Detection and Ranging (LiDAR) technology to conduct bathymetric surveys of shallow water regions. There are various bathymetric LiDAR systems. The majority make use of a green laser for the bathymetric survey. However, the availability of these devices is still relatively low, mostly due to their high prices. LiDAR is only feasible for relatively shallow and clear waters. During the study of two lakes in Poland, it was concluded that a LiDAR sensor can be used for measurements in the littoral zone (up to 1.6 m), and in deeper areas the accuracy significantly decreased . In anotWe took the shoreline detection approach for this study using photogrammetry. The drawback of this method is that it requires multiple surveys, but it was possible to determine the shoreline in areas containing aquatic macrophytes where other methods would fail. It is much easier to conduct RS surveys in difficult areas, and surveying is much quicker compared to traditional surveys. Furthermore, traditional surveys cannot match the resolution of the data collected by remote sensing (depending on the RS data source). There are many different techniques for shoreline detection, as was explored in the Methodology section.Different water segmentation methods were implemented, including machine learning, to establish the optimum methods for shoreline detection in shallow nearshore areas with different conditions. A very interesting comparison and assessment of different object-based classifications using machine learning algorithms were presented in the article . The stuOur study demonstrated that image classification and thresholding of various indices can be used to highlight pixels that represent water in areas containing aquatic macrophytes. It works well in areas with medium-density vegetation, but using these techniques in the areas covered with dense aquatic vegetation often does not clearly separate the shoreline. Shorelines can be approximated by judging the density of water pixels in the aquatic vegetation, but it is difficult and not as accurate as in areas that are clear from vegetation.There is no universal method to detect shorelines using RS, i.e., in different conditions, different methods are better-suited. NGRDI thresholding showed the best results in areas with shallow and sandy shorelines, and supervised classification was typically the best method for separating water from vegetation. Unsupervised classification of NDWI was also accurate and performs relatively quickly if multispectral images are available. These findings make surveying more accessible with simpler equipment. Moreover, when assessing shorelines with washed-up silt, a visual analysis of orthomosaics was the most reliable method because the other techniques failed to separate these areas without introducing excessive noise to the segmented images. In difficult conditions with dense aquatic macrophytes, shoreline detection was very complicated. It was necessary to double check the results of all water surface identification techniques, because each of them had some percentage of incorrectly identified pixels (noise) and it was relatively easy to incorrectly identify the shoreline. In such conditions, traditional manual bathymetric surveying is the most reliable method, but it is very challenging.2 at normal water level. Moreover, various spawning sites have different morphologies and nearshore conditions, so these results cannot be applied to the entire reservoir. The purpose of this research was to explore the possibility of utilizing alternative surveying methods and to compare the outcome with traditional morphological surveys. The successful implementation of modern surveying and analysis techniques could provide very useful insights to assist in improving reservoir storage usage while increasing power generation. The research area of approximately 5 ha is very small compared to the total area of the reservoir, which is 63.5 kmAfter comprehensive data analysis, it was determined that NDWI was the best-suiting index for highlighting water from the vegetation . NGRDI also performed very well for the orthomosaics that did not contain NIR. The OA of unsupervised classification of NGRDI was 92% and KC 0.837. These findings can be adopted for further research in areas with similar conditions that are common for the reservoirs in the lowland countries. The results of the spectral signature analysis suggest that image segmentation could be carried out using supervised classification. Medium resolution multispectral mosaics are available for most parts of Lithuania (GSD is approximately 0.2 m); therefore, future research will focus on using the findings of this study for the automatic detection and mapping of potential fish spawning sites over the entire reservoir and for assessing the degree of accuracy. This information would be invaluable, as current assessments of spawning sites in the reservoir are mostly based on studies that were carried out in the 1990s.


The diagnosis of leukemia relies very much on the results of bone marrow examinations, which is never generally performed in routine physical examination. In many rural areas even community hospitals and primary care clinics, the lack of hematological specialist and facility does not allow a definite diagnosis of leukemia. Thus, there will be a significant benefit if machine learning (ML) models could help early predict leukemia using preliminary blood test data in a routine physical examination in community hospitals to save time before a definite diagnosis.  We collected the routine physical examination data of 1230 newly diagnosed leukemia patients and 1300 healthy people. We trained and tested 3 machine learning (ML) models including linear support vector machine (LSVM), random forest (RF), and XGboost models. We not only examined the accordance between model results and statistical analysis of the input data but also examined the consistency of model accuracy scores and relative importance order of model factors with regard to different input data sets and different model arguments to check the applicability of both the models and the input data.  Generally, the RF and XGboost models give more identical, consistent, and robust relative importance order of factors that is also accordant with the statistical analysis, while the LSVM gives much different and nonsense orders for different inputs. Results of the RF and XGboost models show that (1) generally, the models achieve accuracy scores above 0.9, indicating effective identification of leukemia, and (2) the top three factors that contribute most to the identification of leukemia include red blood cell (RBC), hematocrit (HCT), and white blood cell (WBC), while the other factors contribute relatively less.  This study shows a feasible case example for early identification of leukemia using routine physical examination data with the assistance of ML models, which can be conveniently, cheaply, and widely applied in community hospitals or primary care clinics to save time before definite diagnosis; however, more studies are still needed to validate the applicability of more ML models to a larger variety of input data sets. Leukemias are a group of life-threatening malignant disorders of the blood and bone marrow . UsuallyThe prognosis of AL is poor, and the death rate of AL is dramatically high. Its complications are usually life-threatening, and its treatment is generally complex . FurtherClinical diagnosis of leukemia is generally according to the cytomorphology, immunophenotyping, cytogenetics, and molecular genetics of the bone marrow and blood samples , which hIn this case, early screening and treatment of leukemia patients in time can be very important. Certainly, a good solution for this task is to evaluate the health condition of individuals according to their regular physical examination data, which is complicated and difficult because it requires the experiences of physicians and careful subjective judgement of the complex relationships among various test parameters. In contrast, machine learning (ML) models are right designed to be expert at this task of determining complex relationships. They can handle even thousands of parameters, and they are able to detect and utilize their interactions –11, whicML model is a practical and versatile choice for the early screening of diseases. It has achieved significant development and is successfully applied to a wide range of data-related problems . For exaIn this study, we aimed to try utilizing ML models for the early diagnosis of leukemia using only the individual routine medical examination results. The advantage of doing this is thus ML models can be conveniently, cheaply, and widely applied in community hospitals or primary care clinics and can save time as much as possible before disease progression. We hope this work could provide a feasible case example of using ML models to early screen leukemia patients.The ML models require both training and test data. In this study, we employed the routine laboratory test results of blood samples of both leukemia patients and healthy people to train and test the ML models. The routine laboratory records of blood samples were collected from the database of the first affiliated hospital of Chongqing Medical University, including those of the leukemia patients from the department of hematology admitted during 2014.4∼2020.6 and those of healthy people from the physical examination center during 2020.1∼2020.6. The data collection was performed under the approval of their Medical Ethics Committee (Number: 2021-152), according to the principles of the Declaration of Helsinki. Besides the blood test records of the leukemia patients, we collected their personal information and medical histories as well. As it is very common for leukemia patients to receive treatment repeatedly, we kept only the blood records at their first admission to our hospital but waived those afterwards. Moreover, we double-checked their medical histories to exclude those patients who were already diagnosed and treated before in other hospitals.After those efforts, we screened out totally 1230 identified leukemia patients, and accordingly 1230 blood records, with totally 284 parameters tested at least once. The largest number of tested parameters appearing in a single record is 134, while most records contain around 50 tested parameters. These blood test records of leukemia patients were scheduled to be the target group in the ML models.For the control group of ML models, we randomly selected 1300 blood records of healthy people from the physical examination center of our hospital. Their medical histories were also checked to ensure they were not leukemia patients, but even so, strictly speaking, we did not exclude those who had been with leukemia but were not diagnosed yet. Hopefully, such probability is very limited. The blood test records of healthy people contain fewer tested parameters than those of the leukemia patients. Therefore, we had to keep only the intersection of their tested parameters (30 parameters in common) into the following steps  of the 30 tested parameters were calculated , using mML models have significant benefits for the preliminary screening of diseases. However, generally, it is hard to say which model is absolutely the best, because model applicability depends on specific data set. Usually, in practice, various models will be tested, and their results will be examined to determine which model performs the best.In this study, we chose 3 ML models to be tested and examined: linear support vector machine (LSVM), random forest (RF), and XGboost models. The main reason for choosing them is because they are relatively much more popular and have shown good performance in various applications, and also because they are able to give relative importance to model input factors as well.XGBClassifier of package xgboost in Python.We utilized the very popular scikit-learn (sklearn) package (version 0.24.2 in Python 3.9 for Mac)  for LSVMAs the applicability of each ML model depends on specific data set, their model results have to be examined to check their applicability. Practically, firstly, a reliable model should show consistency and robustness with regard to its input data, and secondly, its result should be accordant with the results of other analysis method like statistical analysis as well.Rtrain/total = 0.25,0.5,0.75), and accordingly, their results, including the scores of score function of sklearn which returns the accuracy of the model on input data [Sauc), as well as the relative importance order of model factors (order of contribution weight of specific factor to the model), would all be examined to check their consistency with regard to various input data sets.Since the model input data of this study mainly depend on the scenario selection see  and the put data , the arep < 0.001 for all unadjusted comparisons). According to the p values, most of the parameters show a significant difference between the two groups, and generally, the HCT, HGB, and RBC show the most significant differences among those parameters.As shown in p values of many parameters show significant statistical differences between patients and healthy people, for a certain individual, it is hard to diagnose a person with leukemia or not only according to a single or even two parameters, because many values of the parameters of the patients still lie in its reference range. Thus, for diagnosis, more parameters need to be taken into consideration, which requires the determination of more complicated interrelationships behind. That's exactly what ML models are adept in.Although the lambda of the XGboost model and the argument C of the LSVM model were adjusted to suppress overfitting, and the model performance indicators including the accuracy scores on both train and test subsets  and the Sauc were collected in Tables 2Sauc suggest all models have achieved good results because generally, the accuracy scores are mostly above 0.9 even when the Rtrain/total is 0.25. Among scenarios, the accuracy scores are higher under scenario A, indicating the filling of missing values induces more uncertainties than discarding incomplete records.During modeling, the argument lambda. However, the importance order of the LSVM model is much different and is much more sensitive to the input data and the overfitting suppression factor C.For checking model result consistency, although the accuracy scores in Tables 2#), percentage of monocytes (MONO%), and NEUT% , and they are capable of detecting and utilizing the interactions among these numerous attributes, which makes this field of medicine particularly interesting for ML applications .Nowadays, ML has already been proven to be a versatile, precise, and robust tool in the diagnostic evaluation of leukemia . Rehman As most of the ML application on leukemia diagnosis was dealing with the microscopic images and flow cytometry of bone marrow or peripheral blood cell, there is a lack of early prediction ML model for leukemia based on routine laboratory results. In this study, the required data for the ML models we used are able to be commonly acquired from the very primary routine physical examination in the rural area, community hospitals, or primary care clinics, which could help the early recognition of leukemia.C (adjusted during modeling) is specified, and for the RF model, only the number of trees (we set n_estimators = 200) is set, and for the XGboost model, only the learning rate (we set learning_rate = 0.05) and the overfitting suppression factor lambda (adjusted during modeling) are required. Results show that the RF and XGboost model achieved very good consistency and robustness because their results turned out consistent and are accordant with the statistical analysis. As for the bad result of the LSVM model, we would like to regard the reason relevant to the limitation of its linear kernel to its applicability in this case of our study, but we did not check further into it.As the applicability of a certain ML model depends upon specific input data set, in this study, three models including the LSVM, RF, and XGboost models were selected, and their results were examined to check their applicability. The reason for choosing them is because they are relatively more popular, and more importantly, the sklearn toolkit we employed could look into the relative importance (or say contribution weight) of each model factor to the model, from which we could both examine the most model details and find the top factors that play key roles in the recognition of leukemia. Another consideration is that these three models require relatively much less input argument during the model construction because more arguments usually lead to higher model sensitivity to these input arguments. Specifically, for the LSVM model, only the overfitting suppression factor In order to deal with the missing values of the incomplete records, we checked the difference between rather discarding the incomplete records and filling the missing values with an estimated average of existing values. Results show that the filling of missing values using estimations tends to introduce more uncertainties than directly discarding these incomplete values. This is interesting because, in other literature, many authors follow the filling method without any discussion or examination. We believe that the difference between filling and discarding should be case-dependent, and we should pay more attention to dealing with missing values.The results of the RF and XGboost models also show that in this study, the accuracy scores are generally at least above 0.9 on both the train and test subsets even when the train data are a quarter of the total input. This might be partly relevant to the capability of the RF and XGboost models and partly be relevant to the accuracy and specificity of the input data as well, because the data we collected are from either very healthy people or from relatively severe patients. Therefore, about the methodology of this study, more further work is actually still needed to check the applicability of more ML models including SVM and other ML models to a larger variety of data sets.The top 3 model factors that contribute most to the recognition of leukemia are the count of RBC, HCT, and WBC. The other factors contribute relatively less to the models.The result about the WBC's count sounds reasonable that, as leukemia is a blood cancer that usually begins in the bone marrow and leads to the overproduction of abnormal WBC , the insIt also makes sense about the count of RBC and HCT. The RBC, transporting oxygen and carbon dioxide , 37, mayGood results have the ML models get. Although it should also be emphasized that the results of ML models can only be an auxiliary reference but have no opportunities to replace the definite diagnosis by physicians, the real advantage of ML models is that the ML models can be conveniently and widely applied in the routine physical examination in community hospitals or primary care clinics without much extra expense and can save much of the time before disease progression, because if the routine physical examination result of somebody was classified by ML models as potential leukemia in time, he would be suggested to visit specialized hematology physicians as soon as possible for a specialized examination.In this study, we conducted a retrospective case study of utilizing the ML models to help early diagnosis of leukemia using only preliminary blood test data from the routine physical examination at community hospitals or primary care clinics. We collected data of preliminary blood test of both newly diagnosed leukemia patients and healthy people to construct the train and test data sets for ML models. We selected three models including LSVM, RF, and XGboost models according to their popularity, application convenience, and their ability to tell the relative importance or contribution weight of each factor to the model. We examined the sensitivity of model results, including the accuracy score, the area under the curve score, and the importance order, to the model input data and model argument including the scenario selection, split ratio, and the overfitting suppression coefficient.Results show that although the LSVM expressed very bad applicability to the input data of this study, the RF and XGboost turned out of good consistency and robustness with regard to the input data and model argument, and their results are also accordant to the result of statistical analysis of the collected data. Generally, the RF and XGboost models could achieve an overall accuracy score above 0.9 for all the input data we used in this study. The top three model factors that contribute most to the recognition of leukemia are the count of WBC, HCT, and RBC, and the other factors contribute relatively less.This study is a feasible case example to show that leukemia can be early predicted using preliminary blood test data from routine physical examination with the assistance of ML models. The advantage of doing this is thus ML models can be conveniently, cheaply, and widely applied in community hospitals or primary care clinics and can save time as much as possible before disease progression. Nevertheless, the results of ML models cannot replace but still require the definite diagnosis of hematology physicians.Technically, there are still a few limitations of this study that affect the confidence of our models: (1) the details about the applicability of ML models to our input data set are still not fully understood; (2) all records were retrospectively collected from the First Affiliated Hospital of Chongqing Medical University, which may cause selection bias; (3) there are potential uncertainties, including uncertainties of laboratory measurements or the possibility of undetected leukemia patient in the healthy group; (4) only 30 parameters are kept in the modeling procedure, while some other parameters dropped might also be a potential indicator of leukemia. Therefore, the result of this study shows a good case for early predicting leukemia using preliminary blood test data from routine physical examination with the assistance of ML models; however, further investigation and prospective studies are still needed in the future to validate the applicability of more ML models to a larger variety of input data sets.


Innovation is fundamental in medicine because it enables researchers to overcome the hurdles related to pathologies. This Research Topic was initiated by Iacoviello et al. and published in 2022 in Frontiers in Cardiovascular Medicine. The collection aims to bring together original research or review studies on innovations in the treatment of chronic heart failure (CHF) as a principal aim or target.Novelties in heart failure drug therapy. Over the last few years, a number of new classes of drugs have been demonstrated to further improve HF patients' prognosis and are currently recommended  and evaluated the effects of SGLT2i on major cardiovascular, renal, and heart failure events. The results add new information about the SGLT2i by demonstrating the superiority of the combination of SGLT2i and inhibition of the renin angiotensin system when compared with the inhibition of the renin angiotensin system alone in reducing cardiorenal events.ommended . In partommended . The metLi et al. analyzed data from two main trials evaluating the effects of SGLT2i in patients affected by CHF with reduced ejection fraction (HFrEF), i.e., DAPA-HF and EMPEROR-reduced, and in those affected by CKD, i.e., DAPA-CKD. SGLT2i significantly reduced cardiorenal events, i.e., hospitalization for heart failure and kidney-specific composite outcome, and increased quality of life, as evaluated by Kansas City Cardiomyopathy Questionnaire, without showing any significant increase in events related to adverse effects such as the risk of volume depletion, fracture, amputation, major hypoglycemia, and diabetic ketoacidosis.The effects of SGLT2i are even more striking when the efficacy and safety of SGLT2i in diabetic and non-diabetic patients affected by CHF or chronic kidney disease (CKD) are considered. The metanalysis of Correale et al. shows new perspectives on HF therapy, mainly focusing on new therapeutic targets such as microcirculation, cardiomyocyte, and inflammation.Despite the effects of the new pharmacological therapeutic approaches, the prognosis of patients remains poor. For this reason, there is a need to identify new therapeutic targets able to personalize and further improve HF prognosis. The review by Passantino et al. reported the most recent principles of exercise prescription for CHF patients, which are to obtain meaningful clinical results, without increasing the risk of adverse events.Besides drug therapy, exercise based cardiac rehabilitation is an approved approach that aims to improve the functional capacity of HF patients. The relevance of comorbidities. Some comorbidities are common in patients with heart failure and affect clinical outcomes. With an elegant experimental design, Hong et al. demonstrated the impact of obstructive sleep apnea on heart function in patients with dilated cardiomyopathy. The suggested mechanism of injury is related to exosomes regulated autophagy .Rizzo et al. The physiopathological, clinical, and prognostic consequences of iron deficiency (ID) are summarized, along with the main results of the FAIR-HF, CONFIRM-HF, and AFFIRM AHF studies, that demonstrated significant improvements in symptoms, quality of life, and clinical outcome after intravenous iron supplementation with ferric carboxymaltose.The clinical benefit of the treatment of another prevalent comorbidity in CHF is reported by Yi et al. that analyzes the association between HF and COVID-19. A deep compression of mechanisms of heart failure in COVID-19 patients is mandatory to identify targets for future therapeutic interventions and reduce the incidence of heart failure and improve outcomes in patients with COVID-19.ID is now unanimously considered a new therapeutic target in heart failure patients. We cannot overlook the ominous association between heart failure and COVID-19 and have included a paper by What is new, from the electrophysiology standpoint. Innovation usually starts from an idea and needs to be tested in an experimental setting. For this reason, we have also included a paper by Zhao S. et al. on “Novelties in the Therapeutic Approaches for Chronic Heart Failure.” Myocardial infarction is one of the most common causes of heart failure: the transition from acute myocardial infarction to chronic heart failure is often unpredictable and is delayed by the established pharmacological and non-pharmacological treatment of heart failure. The authors of this interesting paper focused research efforts on a new non-pharmacological treatment of heart failure, left vagal nerve stimulation. They tested a low level of stimulation, i.e., a level which does not cause significant bradycardia. Routinely used in patients with refractory chronic heart failure as one of the very last therapeutic options , seems to be a promising innovative treatment for pulmonary hypertension, at least in the experimental context. Martin et al. studied the effect of relaxin on sugen related pulmonary arterial hypertension in a group of rats, with a particular focus on the incidence of ventricular arrhythmia and sinus node dysfunction. The authors demonstrated that relaxin reverses the fibrosis process associated with right ventricular hypertrophy and pulmonary arterial hypertension, which significantly reduced right ventricular remodeling and reduced the incidence of spontaneous and induced life threatening ventricular arrhythmias. The strengths of the present study are the presence of a control group and the use of two different doses of relaxin. The last option furthered understanding by indicating that the effect of relaxin is dose-dependent, since the best effects were obtained when the blood concentrations of relaxin were in the range of 15 ng/mL. Thanks to the immunochemistry results, the authors showed that relaxin favors an increase of beta-catenins at the level of the intercalated disks, favoring better connections of myocardial cells and explaining the higher conduction velocities.Another research study published in this Research Topic, on novel heart failure concerns relaxin , deployed in patients with CHF receiving an Abbott® cardiac resynchronization therapy device. This algorithm enables it to adapt dynamically to the programmed atrioventricular delay and was demonstrated in combination with biventricular pacing to significantly narrow the QRS duration. The authors sought to investigate the effect of this algorithm on left ventricular remodeling assessed with echocardiography and on NYHA class. One hundred twenty-two patients were consecutively enrolled by Wang Y. et al. and separated into two well-balanced groups according to the use of the mentioned SyncAV algorithm. The patients in whom the SyncAV algorithm was added to the biventricular pacing obtained an acute improvement of the hemodynamics calculated by aortic VTI and subsequently a better echocardiographic and clinical response at mid-term follow-up. The authors should be congratulated for having designed a very simple study with clear endpoints. The paper is an easy read and confirms once more how cardiac resynchronization therapy could be empowered through novel algorithms that allow more physiological pacing. The main limitation of the study was the lack of randomization. The authors overcame this flaw by performing a propensity score matching within the two groups and confirming the original results.The last and third paper included in the arrhythmology section of the Research Topic concerns the use of a novel pacing algorithm called “SyncAV” (Research in chronic heart failure is a continuous endeavor. We hope to have contributed to these efforts with this Research Topic, opening new treatment pathways that will potentially increase the survival rate of patients with heart failure.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.