Injecting drug use is known to contribute significantly to the spread of the HIV epidemic in many parts of the developing world. Due to the hidden nature and stigma of the problem, it is difficult to study using routine surveys. Therefore, this study aims to estimate the number of people who inject drugs in Addis Ababa, Ethiopia, and to describe the epidemiological and social situation related to HIV among people who inject drugs.The study used rapid assessment methods, followed by combined methods of estimating populations, using nomination and multiplier methods. The combined methods used two datasets: the first includes the proportion of people who use services within a year as a multiplier, and the second, a count of the list of people with a problem who used the specific service within a year as a benchmark. The rapid assessment incorporated different qualitative tools to elicit information related to injectable drugs, using existing data sources, in-depth interviews, and focus group discussions.The study estimated a total of 4068; with 95% CI  people who inject drugs (PWIDs) in Addis Ababa. The study found people who inject drugs were young in age, male, with a lower educational status, unmarried, and living in small clerical business. People who inject drugs and participated in the study were more likely to use additional substances like alcohol, khat, and cannabis. The most common form of injectable drug used was heroin, and most of the people who inject drugs reported sharing syringes and needles. A high proportion of study subjects also disclosed having positive test results for HIV, hepatitis B, and C.The population size of people who inject drugs in Addis Ababa is high. Lack of service in harm reduction in the city has made PWIDs vulnerable and at higher risk for HIV/AIDs and hepatitis B and C. Therefore, responsible bodies must start implementing the essential harm reduction strategies given by the World Health Organization. Injecting drug use is known to contribute significantly to the spread of the HIV epidemic in many parts of the developed and developing world –3. The sHIV risk behavior is higher among women than among men who inject drugs , 10. In A high proportion of people who use injection drugs live also with HIV , 2, 13, People who inject drugs, like other substance users, are at higher risk for serious morbidity and mortality due to an overdose of the drug , 29. StuResearch on people who inject drugs is hindered by methods of estimating the size of a hidden population. Literature shows the lack of a single method which could be described as the ‘gold standard’ technique for population size estimation. The methods used for the estimation of the population of hidden phenomena include the census/enumeration method, simple population survey method, the nomination method, and multiplier methods. Each of these methods has their own advantages and limitations , 34. ComResearch on injection drug use is also challenging due to lack of information on the number of affected people and geographic location over time. Although the existence of people who inject drugs in major cities and towns in Ethiopia is well known, its occurrence and the estimated number are not well documented. Such information could be used to better target HIV and hepatitis C prevention programs among people who inject drugs and to compare the occurrence of injection drug use, HIV, and hepatitis C across the cities –39. ThesThe study used a rapid assessment method, followed by a combined method, using the nomination and multiplier method. The rapid assessment incorporated several components to elicit information related to hotspot sites of injectable drug use within the city. This included in-depth interviews with experts, people who inject drugs and key informants as well as focus group discussions, site mapping, simple observations, and a review of relevant documents.The research was conducted in Addis Ababa, the capital city of Ethiopia, which also serves as the headquarters location for many national and international organizations, including the African Union. It is a center for more than 120 embassies and a place of residence for many tourists. The city has a total population of 5.65 million (projected for 2017), with an average density of 5646 people per square kilometer .The source population for the research included people who inject drugs and others who are linked with this group. To estimate the number of people who inject drugs, the source population were people who inject drugs, with the following inclusion criteria: at least 18 years of age, who use an injection drug for non-medical purposes, who stayed in the study city for at least the last 6 months, and who used an injection drug at least once in the last 3 months. Cases who fulfilled the above criteria and who were captured by the respondent-driven sampling method were assessed for injection drugs use with a confirmatory checklist. The confirmatory checklist included (1) knowledge of injection sites on the body; (2) knowledge of where to obtain injection rigs; (3) knowledge of the size of syringes and needle and on how to use it; and (4) assessing scar at the point of the last injection on the body. Eligibility was assessed by a trained coordinator. People who inject drugs were sampled through a respondent-driven sampling (RDS) approach for face-to-face interviews until saturation was reached. Respondent-driven sampling is a variant of chain referral sampling and was used to recruit peers. When implemented and analyzed properly, RDS can provide estimates representative of the networks of the populations sampled , 42. TheSince the study estimated the total population who inject drugs, sample size determination was not considered. However, maximizing inclusion of people who inject drugs was necessary. In the combined methods, for both the nomination and multiplier methods, all people who inject drugs and captured by the respondent-driven sampling in the given time period were included. For the qualitative methods, data collection continued until saturation of information related to the research questions was reached. In each enumeration site, three individuals who inject drugs who were selected by the rapid assessment were selected as seeds for the respondent-driven sampling approach. Emphasis was placed on ensuring diversity among the individuals selected s seeds. Each seed was oriented individually on the objectives of the study, and the use of coupons to recruit three eligible participants. After enrolling and completing the behavioral questions, each seed was given a fixed number (three) of coupons o recruit other peers with the same behavior of the seed as part of the first wave of recruitment. The second wave of recruitment included people who inject drugs, who came with a coupon of recruitment provided by recruits included in the first wave of recruitment Fig. .Fig. 1SEach recruit was interviewed for the estimation, on questions related to behaviors and distribution of the problem in PWIDs. All recruits were subsequently advised to recruit another maximum of three peers who inject drugs. The successive trend of recruitment was ensured in long recruitment chains of people who inject drugs, and it continued until it was difficult to find any additional recruits of PWIDs. The recruitment process of study subjects was monitored through the unique number-coded coupons provided on each participant’s recruitment coupon.For the primary objective, to estimate the population size and prevalence of people who inject drugs, using the nomination method, participants were further interviewed through asking two questions, broadly of the following sort: “How many friends of your own, who inject drugs regularly in the last year, do you know?” and “How many of these you know have received a treatment service, due to problems related to the drug, in the last one year in a government hospital in the city?” From these two answers, the multiplier was estimated for health facilities as benchmarks. The multiplier was multiplied by total PWIDs in the selected health facilities who used the service in the last year as a benchmark.To find the benchmark for the city, the research group assessed the number of people who used treatment facilities due to problems related to their use of drugs within a year of the current data collection. Using data extraction from the logbooks of seven hospitals in Addis Ababa, the total number of PWIDs residing in the city was extracted to use as a benchmark. Multiplying the two sets provides an estimate of the probable size of the population in the community. During data collection, descriptive information was collected from study subjects found during the assessment.Within each enumeration site, two enumerators with previous data collection experience conducted face-to-face interviews. A questionnaire with unique identifiers for each respondent, socio-demographic characteristics, type of drugs injected (including frequency and recency), and geographical and social locations of injecting drug users (village level) was developed. The data collectors were trained on safety and how to approach people who inject drugs. Because of its sensitive nature, the training included information on counseling services, providing a coupon for cases, safety, and ethical issues in addition to the content of the questionnaire. Great caution was made to avoid repeated data collection of eligible study subjects. The questionnaire was translated into the local language and piloted in a similar setting. Participants were reimbursed for their transportation to take part in the study and for bringing three PWIDs.For the rapid situational assessment, interview guide and the checklist were developed to guide the data collection process. The data collection was led by the principal investigator and a note taker, and interviews were recorded using an audio recorder. The interview guide was developed based on the main themes of the study and included questions on the social, legal and policy environment shaping patterns of injection drug use, and linked health risks; the extent and nature of access to care and HIV prevention services among people who inject drugs; and the HIV prevention and harm reduction interventions in need of development and their likely possibility. The data collector was free to explore emergent issues in the field while using the interview guides. All qualitative interviews were assisted by the rapporteur and some were audio-recorded with the consent of participants. In addition, a document review was conducted in close consultation with the facility staff.Data collected for estimation of the number of people who inject drugs were entered into a computer using EPI-DATA version 3.0 software package with a double-entry scheme using a programmed entry template, and validated on a daily basis. Descriptive analysis was conducted to assess demographic characteristics and assess reported problems encountered among people who inject drugs. For the estimation of people who inject drugs, both the multiplier and nomination methods used data from two independent sources to examine the overlap between the two sources. The first source was a count of PWIDs from abstracted logbook treated in the last year from seven hospitals in Addis Ababa, as a benchmark, and the second source was the proportion of PWIDs who were considered to use the above hospitals in Addis Ababa, in the last year.To estimate the total number of people who inject drugs using either nomination or multiplier methods, the total number of PWIDs using the seven hospitals was divided by the multiplier. The total number of PWIDs using each hospital, although called the benchmark is similar for each method, the difference is in the estimation of the multiplier. Furthermore, the 95% CI of the multiplier was made by taking the 95% confidence level and considering the multiplier and the total sample.The following formula was used to estimate the multiplier for the nomination method:Where C1, C2, C3, …. Cn was the number of nominee PWIDs who use a specific the seven hospitals, within a year time, by each individual participating PWID in the survey; whereas M1, M2, M3,…. Mn was the number of nominee PWIDs known by each individual who participated in the survey. The calculation for the multiplier method was the proportion of PWIDs in the survey who claimed to use service provided in the hospitals within a year as the multiplier for the benchmark. To synthesize a single estimate, the multipliers were pooled estimates, calculated with the fixed model effect, giving weight to the size of people in the survey [multiplier method] and size of a total number of nominees PWIDs known by participants in the survey. The benchmark was calculated by summing the total count from each hospital. The total population estimate was calculated using a simple excel calculator in Microsoft Office. The single estimate was further supported by its 95% confidence limit.The qualitative interviews, for the rapid situational assessment, were transcribed verbatim and then translated into English by the qualitative interviewers. The English version was prepared in text files and entered into epi.dat computer software to handle qualitative data. Data analysis was made by grouping into the main themes of the study in Microsoft Excel. Based on the deep understanding of the context and the findings from the quantitative study, the qualitative findings were used to complement the quantitative finding and for triangulation of the findings.The study included 276 people who inject drugs. Almost three-quarters of study subjects were below 35 years of age, with a mean age of 29.8 years and SD of + 8.1 years, ranging between 18 and 67 years. The majority, almost 9 of the 10 study subjects, were males, and two-fifths had not completed elementary classes. About half of the study subjects reported any form of employment. Clerical jobs were most common, but a substantial proportion of study subjects were students in high school and universities  people who inject drugs. Synthesized estimation was calculated by taking a pooled estimate using a fixed-effects model, and a total of 4068  adult people who inject drugs were available in Addis Ababa (Table The majority of study subjects from Addis Ababa (72.5%) reported reusing needles. Substantially, a higher proportion (18%) of study subjects claimed to reuse syringe and needle, once, twice, or more times. Moreover, study subjects disclosed sharing needles with other people, in some cases up to four or more people and some of which were unknown to the study subjects Table .Table 6People who inject drugs were asked whether they had been tested for certain diseases. Of the 177 who reported having been tested for HIV, 70 (39.5%) disclosed as having HIV-positive results. Moreover, of the 99 people who reported having been tested for hepatitis B, 37 (37.4%) reported having the disease. Of the 46 who reported having been tested for hepatitis C, 13 (28.3%) reported testing positive for the disease. Of the 171 who reported being tested for other sexually transmitted diseases, 66 (38.6%) disclosed receiving a positive test  people who inject drugs in Addis Ababa, Ethiopia. As this is a hidden population, and there are methodological challenges to estimating the magnitude of people who inject drugs, our findings may represent an overestimation or underestimation. Although we had planned to collect data from health providers and police detention registers as a benchmark, obtaining information only from health providers may have affected our estimates and limited potential comparisons. However, our estimation methodology used the nomination and multiplier methods and generated a pooled measure of the two estimates and their confidence limits in order to obtain a proxy estimation of PWIDs in the city. However, in the measurement of the pooled estimate, a fixed-effects model of pooling the multiplier, and providing a narrow confidence limit may underestimate the number of people who inject the drug in the city. The other problem that may not be corrected here was the difficulty of representation of PWIDs from the rich community who may not be assessed by respondent-driven sampling with such relatively low incentive.The study found people who inject drugs to be young in age, male, with low educational status, unmarried, and working as clerks in small businesses. People who inject drugs who participated in the study were more likely to use additional substances such as alcohol, khat, and cannabis and were using the substances frequently.As young people are prone to risk-taking behaviors, and cities in developing countries have a high proportion of young people, it was expected that youth would comprise much of our sample. Furthermore, many studies also support that young people are prone to substance use –45. SimiOur study has also reports people who inject drugs in Addis Ababa to have a lower educational level and work in a small clerk business; although the study population did include people with all levels of education. This may be due to the fact that there is a proportion of uneducated people in the cities, as evident in the census , 46. OthMany study subjects started injection drugs at a young age and reported frequent use at least once per week. Initiation of drug use at an early age may be related to the fact that many youth engage in high-risk acts when they are encouraged by their peers. Injection drug use initiated among the youth has been reported in other studies , 48.The most common form of the drug used in the city was heroin, and the dominance of one or two types of drugs in the community may be due to the clustering effect of living in common localities. Although the majority of the PWIDs inject a single form of the drug, some PWIDs report taking two or more drugs. The major source of drugs among the PWIDs was obtaining drugs from friends. The hidden nature of the drug use may have made users depend only on a few people for their supply. PWIDs also repored that drugs could be found in special shops or it may be found after it is smuggled. These drugs are considered to be expensive, and drug use could disrupt family life, with some PWIDs having to leave their job, to sell their cars and houses and to divorce or get separated from their marriage and leave their family dispersed in streets , 51.In the quantitative data, only a few study subjects disclosed sharing needles; however, the qualitative data suggested a higher level of needle sharing. Needle sharing is a common phenomenon among PWIDs and increases risk of HIV transmission. People who inject drugs usually buy and use drugs communally. Such use of drugs in groups is important to minimize the costs of the drugs and needles and syringes. PWIDs also disclosed taking an injection in common as a sign of sensing a commonness and friendship.In this study, data from Addis Ababa showed high reports of positive tests for HIV, hepatitis B, and C. Since people who inject drugs report risky behaviors such as needle sharing, the prevalence of these diseases may be higher than the general population. The high prevalence could also relate to lower comprehensive knowledge of transmission and prevention of HIV among people who inject drugs. It should be noted that our study presents perceived diagnoses, which may not be accurate. Finally, the study found the absence of major services related to harm reduction among people who inject drugs in Ethiopia. The stigmatizing and discriminating nature of injecting drug use, augmented by the criminalization of the drug, has resulted in difficulty in reaching people who inject drugs, and in limited access of PWIDs to appropriate services.The study estimated a total of 4068  people who inject drugs in Addis Ababa, Ethiopia. This large estimated population size is of relevance to policy-makers in major cities where large numbers of PWIDs may also be present but hidden. In Addis Ababa, the youth are the most vulnerable population for injection drug use, thus government should assess and initiate strong prevention strategies within instituitions where youth may be found, such as schools . The most common type of injection drug used in this study was heroin, which can be partially treated by opioid replacement therapy. Such replacement therapy should be made available in the city. A high proportion of study subjects reported having a positive test result for HIV, and hepatitis B and C. However, these results needs confirmation using biological sero-testing method for HIV and viral hepatitis, and indivuals should be linked with the appropriate treatment and care HIV and viral hepatitis.


The recent pandemic caused by the 2019 outbreak of novel coronavirus (2019-nCoV or COVID-19) has affected more than 3.0 million people resulting ~ 212,000 deaths across 215 countries/territories as on 28th April 2020. The importation of the cases owing to enormous international travels from the affected countries is the foremost reason for local cycle of transmission. For a country like India, the second most populous country in the world with ~ 1.35 billion population, the management and control of 2019-nCoV domestic spread heavily relied on effective screening and strict quarantine of passengers arriving at various international airports in India from affected countries. Here, by extracting the data from FLIRT, an online airline database for more than 800 airlines, and scanning more than 180,000 flights and 39.9 million corresponding passenger seats during 4th – 25th March, we show that India experienced the highest risk index of importing the passengers from middle eastern airports. Contrary to perception, travelers from China imposed lowest risk of importing the infected cases in India. This is clearly evident form the fact that while the number of infected cases were on the peak in China India was one of the least affected countries. The number of cases in India started exhibiting a sharp increase in the infected cases only after the European countries and USA recorded large number of infected cases. We further argue that while the number of cases in middle eastern countries may still be very low, the airports in middle eastern countries, particularly Dubai, being one of the largest transit hubs for international passengers, including arriving in India, might have posed a higher risk of getting infected with 2019-nCoV. We suggest that any future travel related disease infection screening at the airports should critically assess the passengers from major transit hubs in addition to affected country of origin. In December 2019, health authorities from Wuhan, Hubei province in China, local hospital were monitoring and reported group of pneumonia cases  and ~ 3.India, the second most populous country in the world with total population of ~ 1.35 billion inhabitants, registered its first case on 30th of January 2020, as a 20-year-old female medical student, along with two other students, travelled from Wuhan to the south-western state, Kerala. Since then India has registered ~ 657 cases till 25th March, out of which ~ 62% were reported to be the imported cases travelling from various other affected countries. Eventually, India then suspended all international flights to curb the domestic spread due to 2019-nCoV on 25th March 2020. Right from the early days of the pandemic, body temperature screening to verify if the traveler has a fever, as an indication for the infection, as a major test was introduced at all international airports in India. However, it has been noticed that asymptomatic contact transmission of 2019-nCoV and passengers successfully passing the symptom-based screening have been tested positive for 2019-nCoV [The timing of 2019-nCoV outbreak and aftermath encompassing the lunar new year celebrations in China coincided with very large amount of human movement between China and rest of the world imposing major threat for rapid spread of infection . FurtherIn the present study, to calculate the importation risk of 2019-nCoV infection we collected the number of passengers for given airports of an origin country travelling to various international airports in India. We also accounted the total number of 2019-nCoV infected cases in the country of origin. Information about the total number of infected cases in the country of origin was obtained by extensive online search of more than five different sources, which mainly included the government sources and were rigorously verified and validated for any inconsistencies. To obtain the count of air passengers from a origin airport in given country, we relied on a web-based analytical tool called FLIRT [The FLIRT web application allows two different types of flight data simulation. The first mode includes the simulation of total seats offered by airlines between origin and destination airports, and the other mode simulates the total passengers travelled between airports. The second mode, however, has a significant disadvantage that it allows only 20,000 passengers to be simulated at a time, which is an upper bound on the total number of passengers that can be simulated. Such an upper bound can have a strong bias in the analysis, particularly for this kind of studies. Therefore, the first simulation mode was preferred here. This selection would not affect the analysis, as FLIRT assumes that the number of seats between two airports is directly proportional to the number of passengers between them . Thus, fa is the transmission rate, which is assumed to be 2.5 (recommended by WHO), P is the total number of India bound seats from the international airports in the origin country, and T is the total number of seats in all the airlines originating from the selected airports or origin country. I is the total number of 2019-nCoV infected patient in the origin country and N is the population of the origin country. The Importation Risk Index is a weighted risk number and is proportional to the risk possessed by a country to import 2019-nCoV cases into India.The specific methodology to derive importation risk index for a for a given destination was demonstrated by many researchers , 10. BasThe estimation was carried out for 24 airports selected across ten countries, and a Pearson Correlation coefficient test between the estimated risk index and the total number of imported 2019-nCoV cases in India from these countries up to 24th March, as reported by the government, was performed. The detailed results are presented below.R2 = 0.99) clearly indicates the strong agreement between number of passengers imported from a specific country and corresponding importation risk index. There is approximately factor of 6 difference between highest number of passengers  and second highest from United Kingdome (~ 50 passenger). To avoid any bias in the representation of correlation we have also plotted the scatter plot between all the countries (excluding middle east) and importation risk index .In this analysis, a total number of 187,755 flights were simulated across 24 airports and ten countries estimating around 33.9 million passenger seats. The importation risk indices of the individual countries for various airports estimated are shown in Fig. We used the various online sources along with the data from government organizations for ten countries to screen the flights and passengers form 24 airports connecting various international airports in India. As of 4th March 2020 India has started the thermal screening of passengers arriving from 12 countries, viz. China, South Korea, Japan, Italy, Iran, Singapore, Thailand, Malaysia, Hong Kong, Vietnam, Nepal, and Indonesia. It is, however, not clear if the passengers either travelling and/or transiting through any of the airports form middle east, especially Dubai, were also being subjected to thermal screening. If that was not the case, it is very important to mention that non-screening of passengers arriving through large transit hub like Dubai (DXB) and were infected could have remained undetected, which later on went on to became positive cases. On the other hand, the stamping of the passengers initiated by authorities in India came as one of the most effective steps, as long as it was stringently executed by authorities and followed by passengers for the self-quarantined for the prescribed period. We strongly emphasize that this was one of the most effective way to contain the spread due to importation of the infected passengers in two following ways. a. a full database of the passenger was being obtained at the time of the screening, and b. it is much easier and feasible to track the very same passenger for next 14 days, which is the advisable quarantine period. Such a screening and additional measures like stamping need not be restricted for the passengers arriving only from largely affected countries but must be extended to the passengers coming from large transit hub in future. For example, at the time of initiating the thermal screening for the passengers arriving from Singapore, there were not many cases registered in Singapore or other east Asian countries. As per our understanding such a measure was implemented primarily to identify the passengers arriving from China, which was epicenter of pandemic, transiting through one of the east Asian countries particularly Singapore airport. Overall, we believe that the effective measures taken by the government and authorities at various international airports in India were adequate and effective, although they were restricted only to the passengers arriving from largely infected countries. We, however, also note that the screening and quarantining of the passengers arriving from major transit hub could also have been strictly implemented from the very beginning. We also recommend that international flight operations in India could be cautiously opened in phased manner. We are also of the opinion, based on our analysis, that only one airport can be considered for international operations until the global pandemic is substantially brought under control. Neverthless, these policies are just suggestions and can be reviewed periodically.  It is very important to note that analyses presented here involve the modeling and estimation based on combination of various different data sources. Under such a situation, where large datasets from various sources is combined to derive certain index, requires a lot of assumptions to consider, and pose various limitations. All these simulations and subsequent estimation heavily rely on the accuracy and reasonable assumption of passenger seats and flight data. In this way, we are not aware of any particular methodology to validate our model, except that comparing the derived importation risk index with actual number of passengers, which for our study is in good agreement. Nevertheless, our results provide data and methodology, which in a nominal way could be used for effective measures and mitigation policies to be implemented at travel hubs for effective containment of travel related risk of infections. Lastly the study presented here completely relies on the data available at this period of time and can be further tuned in future as and when additional data is available, which may substantially alter the results/conclusions presented here.


The rising prevalence and global burden of diabetes fortify the need for more comprehensive and effective management to prevent, monitor, and treat diabetes and its complications. Applying artificial intelligence in complimenting the diagnosis, management, and prediction of the diabetes trajectory has been increasingly common over the years. This study aims to illustrate an inclusive landscape of application of artificial intelligence in diabetes through a bibliographic analysis and offers future direction for research. Bibliometrics analysis was combined with exploratory factor analysis and latent Dirichlet allocation to uncover emergent research domains and topics related to artificial intelligence and diabetes. Data were extracted from the Web of Science Core Collection database. The results showed a rising trend in the number of papers and citations concerning AI applications in diabetes, especially since 2010. The nucleus driving the research and development of AI in diabetes is centered around developed countries, mainly consisting of the United States, which contributed 44.1% of the publications. Our analyses uncovered the top five emerging research domains to be: (i) use of artificial intelligence in diagnosis of diabetes, (ii) risk assessment of diabetes and its complications, (iii) role of artificial intelligence in novel treatments and monitoring in diabetes, (iv) application of telehealth and wearable technology in the daily management of diabetes, and (v) robotic surgical outcomes with diabetes as a comorbid. Despite the benefits of artificial intelligence, challenges with system accuracy, validity, and confidentiality breach will need to be tackled before being widely applied for patients’ benefits. Diabetes is a chronic medical disease that is characterized by increased levels of blood glucose, which causes microvascular and macrovascular complications with time. This chronic condition is concerning because the prevalence of diabetes has been steadily increasing for the past three decades. In 2014, the worldwide prevalence of diabetes was 8.5% among those aged 18 years and older, a escalation from 4.7% in 1980 [The global health expenditure on diabetes among people of ages 20 and 79 is expected to rise from USD 376 billion in 2010 to an estimated USD 490 billion in 2030 [One of the promising research areas that are ongoing in the field of diabetes, is the use of artificial intelligence (AI). Artificial intelligence is a domain in computer science which accentuates the creation and use of intelligent machines that are able to function and respond like humans. According to a recent 2019 bibliometric study, there has been a tripled fold increase in the number of studies on the applications of AI in the past three years alone, with Diabetes being among the top ten areas of interest .The techniques applied in the research of AI and diabetes include machine learning, artificial neural networks, and natural language processing. These techniques have allowed several applications of AI into the diagnosis and management of diabetes such as the diagnosis of microalbuminuria in type II diabetes patients without the need to measure urinary albumin levels. Microalbuminuria (MA) is a known complication of diabetes and is one of the measures of the renal function in diabetic patients The gold standard of such a diagnosis is to collect 24-hour urine albumin excretion, but with artificial intelligence on the rise, the detection of MA can be done with clinical parameters usually monitored in type II diabetes patients such as age, duration of diabetes, body mass index, and HbA1c  [Another promising application of artificial intelligence in diabetes is the development of a model that helps to generate a risk score with the ability to predict future glycemic control in individuals with type II diabetes. Machine learning models have been established and used to forecast the trajectories using clinical parameters such as body mass index, glycated hemoglobin, and triglycerides. This developed model can be used to estimate the patient’s journey with diabetes and determine the follow-up period based on their risk score. Patients with higher risk scores should be followed up more closely compared to those who have lower risk scores to optimize their health outcomes, particularly in diabetes .The evidently increasing interest of academics and practitioners in applications of AI in diabetes management sparked a need for having a comprehensive and up-to-date picture of what has been done in terms of research on this topic, highlighting areas that have been investigated, the emerging research domains, and potential research gaps that need further investigation. Informed readers may, in turn, be able to decide on a better direction for their future researches on the topic. To the best of our knowledge, there has not been a publication looking into AI application in diabetes on a comprehensive, global scale. This study is conducted to fill such gap in literature by adopting a combination of bibliometric approach and more complex analysis of title and abstracts of publications.(a)Step 1: the publications related to AI in medicine and healthcare were extracted ;(b)Step 2: among the papers in step 1, we used terms related to diabetes for identifying studies related to diabetes in AI in health and medicine.We did a search on the Web of Science (WOS) Core Collection, an online database covering the bibliographic data of various research areas since 1900 , and retAll data in .txt format were downloaded from the WOS including title, authors’ names, journal, year of publication, affiliations, a total of citation, keywords, and abstracts. All of these data were converted to xls. file (Microsoft Excel) for checking data error. After this, we filtered all downloaded data by sieving out papers that were: (1) not original articles and reviews, (2) unrelated to diabetes and AIs, and (3) not in English. Two researchers worked independently to guarantee the quality of data download and extraction. Any conflict in terms of paper selection was resolved by discussion. The collective dataset was eventually transferred into Stata 14.0 to be further analyzed .We analyzed the dataset based on the following information: general characteristic , keywords (most common keywords and co-occurrence keywords), and text mining (abstract). After we have downloaded and extracted the data, descriptive statistical analysis using Stata was applied to calculate the number of papers by countries mentioned in abstracts. A network graph that illustrated the connection among authors’ keyword co-occurrence network was created by the VOSviewer . For analyzing the contents of the abstracts, exploratory factor analysis (EFA) was employed to identify and visualize the research domains that stem from all content of the abstracts; to highlight research topics or terms most commonly co-occurring with each other, we used the 0.4. Jaccard’s similarity index. Latent Dirichlet Allocation (LDA) was used for classifying papers into corresponding topics ,10,11,12This study used statistics on papers and citations retrieved from the Web of Sciences databases. No human subjects involved, so that this is not subject to ethical review requirements for biomedical research.n = 3, 1.2%), Egypt , and United Arab of Emirates , the number of papers was small compared to others with less prevalence of diabetes like Ireland, Italy, and Japan.In terms of study settings , there were a total of 36 countries being mentioned in the abstracts of 372 publications see . Of thosBy analyzing the keywords and abstracts’ contents, it provided us with a clearer comprehension of the scopes of studies and development of research landscapes. . Some AI techniques have been most used in this dataset including fuzzy expert system, support vector machine, artificial neural network, and machine learning. Those branches were applied in the following fields of diabetes: (1) clinical prediction ; (2) diabetes management (monitor blood sugar levels); (3) robot-assisted surgery with complication ; (4) the cost of diabetes care.As for the content analysis of abstracts, the top 20 emerging research domains that were highlighted via the exploratory factor analysis of abstracts are listed in Table 4The co-occurrence of the most frequent landscape was shown in In In Based on WOS categories, we identified the dendrogram for those . The horThe study clearly demonstrates a strong and increasing interest in the field of AI in the diagnosis and management of diabetes. This is evident by the increasing number of studies and a considerably higher extent of articles being published and applied. The gain in traction of such studies started from 2010 and has been steadily increasing to 2018, where a record number of 74 papers were published . The nucThe study has found the top five emerging research domains  pertainiUnder this area of interest, the papers published in this category center around the diagnosis of diabetes using branches of AI. The intention of such research is not to replace the diagnosis work done by the doctors in healthcare, but to complement their efforts as such to optimize the patient’s diagnosis timings as well as to cut down on the workload burden.There are many methods published under this category, examples include the diagnosis of type II diabetes via the analysis of parameters such as heart rate variability and arterial blood glucose alterations. They are performed using non-linear methods such as detrended fluctuation analysis (DFA) and Poincare plot to produce two metrics termed standard deviation ratio (SDR) and alpha-ratio. These two metrics are then fed into a machine-learning algorithm to dichotomize the subjects as diabetic or non-diabetic. The paper reports an accuracy of 94.7% in the correct categorization of subjects, which offers the possibility that it could be further developed as a non-invasive screening tool for predicting whether an individual has type II diabetes .Using AI to diagnose different conditions such as diabetes is interesting, but it comes with drawbacks as well. The main problem with AI is that firstly, it needs to be well-trained, which would require a large number of patients in the training set. This is a problem because personal details protection and privacy is a concern. Another problem with AI diagnostics is that it needs to be consistent, replicable, and reliable, but so far many of the studies have not been applying the evidence-based approaches that are seen in established fields .There have been questions about whether the use of AI can improve diagnostic ability in terms of efficacy and time reduction. For instance, a study found the AI model can detect breast cancer in whole slide images better than 11 pathologists, with the pathologists being limited by the allowed assessment time of one minute per image, while the AI is not limited by any factors. However, when the pathologists were given unlimited time, they performed similarly to the AI and detected more difficult cases more frequently than the computers . This raA crucial principle in the management of type II diabetes is to retard the progression of the chronic disease as well as to prevent the onset of complications resulting from the condition. Many barriers to the optimal detection of the progression include patients defaulting appointments, non-compliance to medical advice, and financial barriers that include treatment costs and costs of a healthy diet . TherefoA novel method of achieving the above objective is noted in a paper that studies the application of an artificial neural network model that aims to diagnose type II diabetes mellitus and establishes the relative importance of risk factors. The study was conducted on a cohort of 234 people who were diagnosed with type II diabetes mellitus using glycated hemoglobin levels. A multilayer perceptron artificial neural network that was utilized to highlight the demographic risk factors revealed that risk factors such as age, hypertension, waist circumference, body mass index, sedentary lifestyle, etc. were predictors of type II diabetes. However, the final analysis showed that the most important predictors and risk assessment of diabetes type II were waist circumference, age, BMI, hypertension, stress, smoking, and positive family history of type II diabetes .These risk assessment models developed with the assistance of AI help to improve the detection/screening of diabetes type II by enabling the optimization of health resources for people who are known to be high risk, compared to screening the entire population at large. This not only improves the cost at a population level, but also the landscape of screening for chronic diseases such as diabetes.Monitoring blood glucose levels is a crucial tool to prevent complications such as hypoglycemia, which may lead to coma, seizures, or even death. AI has been applied to analyze breath samples taken from different subjects to determine hypoglycemic states. This was inspired by diabetes alert dogs which could detect hypoglycemia based on their owner’s breath . This prIn today’s day and age, telehealth and wearable technology is catching up and may soon be one of the revolutionary tools used in clinical healthcare. The benefits of such technology are not only convenience, but it also allows the patients to take up more responsibility for their own healthcare. Some of the applications include monitoring of the patient’s vitals through wearable technology such as a watch. In the future, it is possible for AI to be introduced to analyze biological information such as ultrasound scans or electrocardiography (ECG) obtained via the patient’s phone, and provide real-time analysis for the patient. These results can also be shared with the primary doctor or a hospital for further evaluation and action.A mobile application for managing diabetic patients’ nutrition is currently being developed with the help of AI. This application combines AI techniques with a knowledge base constructed from the guidelines of the American Diabetes Association. This application recommends snacks based on the patient’s favorites as well as current diabetic condition, which may help to improve glycemic control and prevent episodes of hypoglycemia .Promoting beneficial behavior change in people with or at risk of diabetes is also a promising area of application for AI . StudiesAI has also been able to assist surgeons in the form of robotic surgery known to come with greater precision, lesser complication rates, as well as quicker healing times. However, that is not all that AI can do in the field of surgery. Machine learning techniques have been known to be applied to the field of surgery, where it concluded that new-onset diabetes and preexisting diabetes are correlated with a decrease in long term survival after liver transplants. Using AI, this has enabled the comparison of different surgical techniques, their complication rates as well as to identify different factors that may affect the outcomes during and after the surgery .The increasingly deeper integration of AI applications in the diagnosis, management, and prediction of diabetes has also come with challenges. Ethical issues, in particular the privacy and confidentiality of patient data, have been a topic of debate in existing literature . It is nThis bibliometric analysis has put forward several key developments in the field of AI in diabetes and has included large quantities of literature on the topic of interest. However, since the study has only included papers written in the English language, a bias towards Western countries may be present. In addition, another limitation of the study was that of a restriction to purely peer-reviewed research publications, which possibly could have affected the extensiveness of the analyzed results. Future studies may also benefit from applying sensitivity analysis for type I and type II diabetes when conducting research using the method applied in this study.The application of AI in diabetes has evidently become more common in recent years, with AI-related technologies being found to assist from diagnosis and clinical treatment to daily management of diabetes. As a condition with severity depending heavily on the lifestyle and behavior of patients and those at risk, continuous monitoring and initiating specific treatments based on data of individual’s conditions and behavior as well as their specific surrounding would likely be effective, and is an area that AI applications can be seen as a promising solution. With such opportunities to enhance diabetes management also come challenges associated with the use of AI, of which privacy and confidentiality remain the major ones. These issues must be tackled and resolved before clinical use of AI-related technology is approved and available for the patient’s benefit.


The use of the Internet to develop new technologies has generated a considerable change in teaching and student learning in higher education. The pandemic caused by COVID-19 has forced universities to switch from face-to-face to online instruction. Furthermore, this transfer process was planned and executed quickly, with urgent redesigns of courses originally conceived for live teaching. The aim of this work is to measure the service quality of online teaching delivered during the COVID-19 period. The methodology was based on an importance-performance analysis using a structural equations model. The data were obtained from a sample of 467 students attending a university in southern Spain. The results reveal five priority attributes of online teaching that need to be improved in order to enhance the service quality of the virtual instruction provided to students. Universities need to redefine their online format by integrating methodological and technological decisions and involving collaboration between teachers, students and administration staff and services. The results do not apply to educational institutions that exclusively teach courses online, but to those institutions that had to rapidly adapt, and shift course material originally designed for face-to-face training. The higher education sector is currently facing new challenges that had never previously experienced, arising from sharper global competitiveness, advances in technology and the rise in the numbers of universities that offer students an ever-broader range of courses to choose from. This has led universities and higher education centers to ponder how they can improve the service quality they offer their students. The use of Internet in education, especially in higher education, has generated considerable change in the way in which learning takes place. The e-learning model for teaching and learning has rapidly developed in recent years, mainly driven by technological advances . E-learnDue to COVID-19, there was a rapid decline in face-to-face teaching in early 2020, with the closure of campuses, schools and other education centers, which saw on-site teaching shift rapidly online. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), more than 1.5 billion students in 165 countries have been affected by the closure of education centers, impacting around 90% of the world student population ; and notThe COVID-19 crisis has presented an opportunity for the development of effective learning solutions . UniversService quality in higher education has been widely researched by managers and investigators due to its importance in economic terms, in reducing costs and gauging student satisfaction ,13. UnivIn higher education, the main clients are the students , an ideaThe main objective of this work is to measure the quality of the virtual teaching of subjects originally designed for the classroom training. The methodology is based on a variant of the traditional Martilla and James importance-performance analysis , as propBased on the literature ,39,40,41This work differs from others in this field, firstly, because it seeks to measure quality in online teaching in undergraduate courses originally designed for traditional or on-site teaching scenarios. The impact of COVID-19 has forced institutions into a real-time restructuring of teaching methodologies and assessment systems originally conceived for classroom training. While universities adapt to these changes, both teachers and students have tried to maintain the access and capacity to acquire knowledge that students enjoyed pre-pandemic. Secondly, this work focuses on a university whose teaching was neither all online nor based on a mixed system before COVID-19, but entirely on-site. Although this university deployed a virtual platform to support face-to-face teaching, classes were nearly all taught on-site. Thirdly, the service quality of the online teaching at this university was not measured by the typical SERVQUAL and SERVPERF methods but by the importance-performance analysis proposed by Picon et al. , which hThe data were extracted from a questionnaire for undergraduate students attending a public University, in southern Spain, which was the inclusion criteria. There were not exclusion criteria as was done in Tzeng et al. . A conveThe data was extracted from a structured questionnaire in which the students had to value the attributes on a scale of 0–7, in which 0 represented “of lowest value” and 7 “of highest value”. The questionnaire contained 14 items grouped as four latent variables. The four latent variables were system characteristics (CAR), effectiveness of online teaching (EFIC), relationship with teachers and evaluation (PROF), and satisfaction with online teaching (SAT). The items were selected from the literature on e-learning and quality of teaching . AttribuThe structural equations model was based on the literature . This moThe data were analyzed using SPSS . The Cronbach’s Alpha coefficient for the value scores was used to determine the reliability of the scales, which came back as higher than 0.7, thus confirming the reliability of the questionnaire. To check the questionnaire’s validity, a factor analysis of the valuations was performed, using the Kaiser-Meyer-Olkin measure, which was 0.75. The null hypothesis was rejected by the Bartlett sphericity test, thus the use of the factor analysis was deemed acceptable. The factor analysis yielded four factors that represent the four dimensions in The sociodemographic characteristics of the sample are showed in The maximum likelihood model was used to calculate the model parameters. Although the data did not comply with the multivariate normality assumption, this method facilitates the convergence of estimates even in the absence of multivariate normality . CriteriThe reliability of the items was measured in order to check that the standardized load factors were higher than 0.7, in order that the variance shared between the construct and its indicator was higher than the error variance ,53, thouThe validity of the measurement model was determined by the convergent and discriminant validity. Convergent validity was measured by the average variance extracted (AVE). Column 5 of The structural model was assessed according to the squared correlation coefficient values and the significance of the paths or regression coefficients. Finally, goodness of fit of the structural model was determined by a set of measures presented in 2 statistic indicates whether the discrepancy between the original data matrix and the reproduced matrix is significant. In this case, the p-value indicates rejection of this hypothesis. However, the χ2 statistic value is strongly influenced by the sample size, the model’s complexity and the violation of the multivariate normality assumption. All the other measures also have values within the limits that allow us to confirm the data’s goodness of fit. To calculate the importance of each attribute, we applied the criteria of Allen et al. [The χn et al. , accordiFor calculating the relative importance of each attribute, the total effects of each latent variable were distributed among the attributes that constitute it, with the standardized regression weights also considered, as shown in For IPA, we needed to know the values for importance and satisfaction in each attribute . As prevAccording to the values obtained in It can be observed that there are eight attributes that fall within the “concentrate on this” area, meaning that these attributes need to be improved. The attributes that require attention are those that are furthest from the diagonal. In this case, the discrepancies show that the attributes most in need of improvement are interaction with other students (attribute 7), levels of concentration in online classes (attribute 6), online tests review system (attribute 11), usefulness of the system (attribute 1) and diversity of activities used in student assessment (attribute 10). There is no attribute in the “continue with the good work” quadrant, while the “low priority” quadrant contains learning speed in online teaching compared to face-to-face teaching (attribute 4). The possible waste of resources quadrant has the online learning autonomy attribute. The results clearly show that there is a general level of dissatisfaction among students with the online teaching rolled out since the outset of COVID-19. Finally, Universities need to adapt to the new situation created by the COVID-19 pandemic. They need to convert face-to-face teaching into an online format that is generally acceptable by their students. Integrating methodological and technological decisions should not just be a task for the higher education institutions as entities but should also involve collaboration among professors, students and administration staff and services. The results of this study suggest a set of priority areas that require attention in order to improve student satisfaction with online training. The first is interaction between students, as the attribute that presented the highest level of discrepancy. Following Ferri et al. , the lacThe second attribute that requires attention is the level of student concentration in online classes. One of the main problems that students face with virtual training is distraction; concentration wavers when sat in front of a computer screen for a length of time. Distraction can take the form of fatigue, email notifications, social network activity, phone calls, unexpected home visits, etc., all of which reduce student productivity and concentration during online classes. To improve student concentration, online sessions could be shorter, mind breaks could be inserted into the planning, along with mini-tasks or short activities. Students’ attention may also be reduced if they have other open apps on the screen during the online lessons. To resolve this, there are apps that can be activated by the institution to block access to other apps not related to the work being done by students in real time online classes.The third attribute that requires improvement in order to raise student satisfaction with virtual learning is the system for reviewing online tests. This is also related to the problematic question of online assessments, in particular the need to know the identity of the student doing the test and to ensure that it is genuine, control over the physical context in which the assessment test takes place, and the relevant data protection legal framework. Many educational institutions are striving to overcome these problems using e-proctoring systems . HoweverThe fourth attribute that requires attention in order to improve students’ satisfaction with online teaching is the usefulness of the system. When the virtual instruction system functions well, students save time in the learning process, it boosts their sense of learning self-sufficiency and can improve results ,32,33,63The fifth attribute to be tackled is the diversity of assessment tests. The lockdowns ordered as a result of the pandemic obliged the majority of education centers to close and switch rapidly from a face-to-face teaching design to one that could be implemented online. Assessment tests have continued to be set, and adapted to virtual learning, but changing the format. Now, however, teachers need to take advantage of the range of assessment activities available online to broaden the range of testing formats, and to increase student satisfaction with the assessment system. The remaining attributes that educational institutions need to attend in order to enhance student satisfaction are of a lesser priority than those already discussed. One such low-priority attribute is the learning speed in online teaching compared to face-to-face teaching. This attribute has a negative discrepancy but low importance among students. This could be due to the fact that students’ knowledge acquisition is a semester-long process because the university courses for which data are available are taught in six-monthly segments, hence the very low importance students attach to this attribute.Online learning autonomy is also an attribute of little importance to the students, although the level of satisfaction is high, thus, it is situated in the quadrant denoting possible waste of resources. Learning autonomy refers to students’ gradual acquisition of their own criteria, methods and rules for transforming them into more effective learners. In online instruction, students establish their own pace of learning by accessing content anytime and as often as is necessary for them to acquire knowledge . This coThis work emphasizes the enormous efforts made by educational institutions to transform content, activities and assessment systems designed for face-to-face teaching to online formats. This was done urgently, and often without the main actors possessing the minimum technological media, nor the digital competences and aptitudes required to adapt to such rapid change . The resThis work contributes to the generation of knowledge about online teaching. The results provide valuable information for education professionals and educational institutions. The challenges posed by online teaching should be more related to the effectiveness of learning and human relations than to the technical characteristics of the systems used. Aspects such as improving interaction between students or between student/teacher, improving concentration or aspects of assessment must be taken into account in order to improve student satisfaction.The situation with COVID-19 should not be seen as a threat by students and educational institutions based on conventional teaching but should serve to adapt the benefits of the online education system to their teaching.At the societal level, online education also has important implications. Aspects such as travel costs and time savings are drastically reduced. On the other hand, for online learning to be effective, students must have reliable access to technology. This requires the development of social and economic policies to break the digital divide in many households and improve access to technology.Like many other investigations in this field, our study has limitations. Although the results of this work can be extrapolated to other educational institutions that have undergone similar changes, the data for this study are based on a single Spanish university. Future lines of investigation would need to compare the data in our study against other universities, both inside and outside Spain. It would also be interesting to study whether differences exist between the levels of student satisfaction with online instruction in terms of course content and areas of knowledge.


Thermal cameras are popular in detection for their precision in surveillance in the dark and for privacy preservation. In the era of data driven problem solving approaches, manually finding and annotating a large amount of data is inefficient in terms of cost and effort. With the introduction of transfer learning, rather than having large datasets, a dataset covering all characteristics and aspects of the target place is more important. In this work, we studied a large thermal dataset recorded for 20 weeks and identified nine phenomena in it. Moreover, we investigated the impact of each phenomenon for model adaptation in transfer learning. Each phenomenon was investigated separately and in combination. the performance was analyzed by computing the F1 score, precision, recall, true negative rate, and false negative rate. Furthermore, to underline our investigation, the trained model with our dataset was further tested on publicly available datasets, and encouraging results were obtained. Finally, our dataset was also made publicly available. Person detection is the backbone of many applications ranging from surveillance and military to traffic analysis. Many computer vision branches like behavior analysis, activity recognition, threat recognition, and person re-identification start with the challenge of person detection.Visual cameras capturing visible light, as well as thermal cameras capturing infrared radiation have been utilized for person detection. Many feature based machine learning ,2,3,4, aIn the last decade, many deep learning based networks ,12,13,14We reviewed the thermal datasets that are available publicly and can be used for person detection. Most of the publicly available thermal datasets see  are eithCapturing and annotating a large amount of thermal data are still challenging. An optimal solution would be to study a large range of data and utilize the tool of transfer learning to learn from RGB data. A different range of phenomena affecting thermal videos in the outdoor environment have not been investigated and described yet. Observing the effect of various data phenomena from thousand of hours of video can help in optimizing dataset development and annotation. The study requires a large dataset recorded over several weeks in different positions and in different places to make sure that all possible outdoor phenomena are covered.As our first contribution, we studied 20 weeks of variable outdoor thermal data thoroughly to find different phenomena that affect the images. Even by determining all the phenomena, it is still questionable what kind of data are going to have a positive effect and which kind will have a negative effect on person detection in outdoor environments while training a network. Generally, it is presumed that the higher the number of images, the better the detection results. However, due to the high variation of the data characteristics and the low resolution of the thermal images, this is not necessarily the case here, as some phenomena might contribute to a high FP rate. To investigate this research question, as our second contribution, we categorized the phenomena and performed an ablation study for each category. This study gave us a deep analysis of the impact of each category of thermal data and let us choose data in an intelligent manner. This analysis was performed using a single shot deep network and the tool of transfer learning. We employed a single shot deep network due to its high performance and fast learning rate. Finally, the third contribution of this article was a new public thermal dataset for thermal person detection that contains variations regarding the time of day, weather, distance to the camera, various body vs. background temperatures, and shadows. The thermal weights will also be available for researchers for further utilization for transfer learning and solving other thermal data problems.The rest of the paper is organized as follows: To create an understanding of thermal person detection, the following provides an overview of the state-of-the-art techniques, as well as the datasets used for the evaluation of these techniques.Hwang et al.  presenteThermal cameras have been utilized in many scenarios ranging from industry to daily life applications . Much reRecently, Herrmann et al.  tested tDifferent multimodal and thermal datasets are publicly available for traffic analysis, surveillance, person tracking, and human pose estimation, among others. The datasets that can be used for person detection are listed in All the datasets available consisted of sequences with a short duration; thus, they had less variability in terms of weather and light conditions. Most of the available datasets were pedestrian data from traffic data analysis and captured from a frontal viewpoint. Many datasets were indoor, and thus, these were captured in controlled light and temperature conditions and did not include all the variability of outdoor environments. Even with a large number of frames  and weatThe first contribution of this paper is the investigation and study of a diverse thermal dataset for person detection. In thermal images, weather conditions have a similar effect as lighting conditions have on RGB images. it is therefore essential to include varying weather and light effects in a dataset. Furthermore, because the resolution of thermal sensors is still relatively low, the size of objects in images is also an important factor. The data we recorded were captured in outdoor sports fields with people playing soccer or performing related exercises. The nature of these recordings ensured that many challenges related to person detection were included: different scales, pose variations, interactions/occlusions between people, and fast and erratic motion. Regarding the weather effects, we recorded 20 weeks of thermal recordings across January to April in Denmark. Therefore, it spanned the periods from little daylight to bright sunny days and snowy days of winter to pleasant spring days. In the recordings, we experienced several different key challenges: varying temperatures (people hotter/colder/same temperature than the ground), shadows (parts of the ground were not heated by the Sun), wind (camera moving), snow (regions on the ground with different reflection and emissivity of heat), and occlusion (people in groups) in the thermal images.After examining all challenges and scrutinizing the entirety of the data, we suggested that nine different phenomena should be included in a dataset for it to be sufficiently diverse and help the model generalize outdoor person detection in thermal images. These nine phenomena are listed and illustrated in We recorded thermal videos from 10 different sports fields for two weeks each, which comprised 20 weeks of data. The cameras used for recording were Axis Q1921 (resolution 384 × 288 pixels) and Axis 1922 (resolution 640 × 480), and they were mounted approximately 9m above the ground on a light pole surrounding the field. Three cameras were installed at the center of each field to cover the entire field area. The sequences selected for this investigation were from all of the cameras’ views. The recordings were done from January 2018 to April 2018.As the first step in transfer learning is a model adaptation, we used 3000 indoor publicly available images  as pre-thttp://www.vap.aau.dk/dataset/.Manually annotating all the data was unrealistic. Therefore, we scrutinized the periods where all nine phenomena occurred, and the number of players in a given image in these periods varied (from 0 to 40). In each period, we selected a frame every 160th second and annotated that frame. This large temporal gap between annotated frames was introduced to enforce as much diversity as possible. One-thousand nine-hundred forty-one frames were selected as the training dataset. In these frames, a total of 5590 persons were annotated. The details of the dataset are presented in A traditional deep learning network contains a large number of parameters. Training such a network requires an enormous amount of training data. The online availability of such an enormous amount of data is not always a possibility, especially in non-RGB applications. Transfer learning is the optimal solution in such conditions since many features in the first layers of a deep learning network are similar across applications . The queYou Only Look Once (YOLO) is one of the fastest deep learning algorithms for the detection of objects in an image, which can process 45 frames per second. This algorithm treats the problem of detection as a regression problem and trains on the whole image at once to optimize the performance. Moreover, it detects the class objects with their probabilities at the same time without requiring region proposals.B number of bounding boxes with their confidence scores could be predicted by each grid cell. Confidence was defined as the probability of detected objects multiplied by the Intersection over Union (IoU) between the ground truth bounding box area and the detected object bounding box area.The YOLOv3 network, used in this work, divided every training image into a grid of , and True Negative Rate (TNR) as the performance measures. Along with recall and precision, we were also interested in true and false negative rates, as these matrices are of great importance in surveillance and occupancy analysis applications, where an event of negative detection is as important as an event as positive detection. The F1 scores of all the combinations are provided in True Positives (TP) were defined as the number of persons that were correctly detected as persons and True Negatives (TN) as the number of images with zero persons correctly identified as having zero persons. False Positives (FP) represented the regions in the image with no person, but there was nonetheless a person detected. False Negatives (FN) represented the regions where persons were present, but the detector failed to recognize them.Results presented in In looking individually at the results of each combinations, one noticeable observation was found with Combinations 2, 7 and 10. These combinations almost had the same performance. Although, if we looked at the number of images in Combinations 2 and 10, Combination 10 had more than three times the number of images as Combination 2. The same pattern could be observed in Combinations 12 and 16. The weather effect contained more than half of the data, but its inclusion increased the performance only by 1%.The overall contribution of each category is also shown in the last row of The results obtained from the experiment are also presented in If we looked at all these combinations, the common category was “heat effects”. The other noticeable effect was the decrease in the dip magnitude with the addition of more categories. As more and more categories were added to “heat effects”, the precision and TNR both improved. There was no significant change observed in the FNR results. However, the recall had an opposite effect from the precision and TNR, as the addition of the “heat effects” category improved recall. The details of this improvement are explained later in the section.The precision and TNR were maximum for the image artifacts and weather effects categories. This was because occlusion and low resolution images were present in the image artifacts category, and the FP and FN reduced; whereas for weather effects, more images of empty fields with snow and shadow were added in the training data. Snow and shadow could sometimes resemble humans and be detected as persons. Therefore, with the addition of the weather effect category, FNR and TNR both improved.Herrmann et al. concluded that an inverted thermal dataset had a resemblance to the grayscale of RGB data. Therefore, the domain adaptation was quicker when pretrained RGB weights were used. In our results, we could also observe a similar response in terms of recall.We can see in To select which category to include in training, it still depended on the target application. For example, if we compared Combinations 12 and 16, the increase in the F1 score was only 0.49% by including the data from the weather effect category. To show the effect of including the weather effect data, a few test images are shown in We picked three public datasets to test the generalization of our trained weights for person detection. The datasets consisted of three different diverse datasets from OSU-T was recorded outdoors with different weather conditions, as mentioned in Tests on publicly available datasets were performed in two sessions. Firstly the images were tested using the weights obtained from Combination 16, shown in For training the second session test, from OSU-T and BU-TIV, we added 5% of the whole data in training corresponding to 14 and 174 images, respectively, and from CVC-09 (day and night), 5% of the training data was added to the training set corresponding to 211 and 160 images, respectively. The number of iterations for learning was 100 to avoid overfitting due to a small number of training images.Results of this experiment are presented in In the CVC dataset, a significant difference between day and night results was observed. During the day, the temperatures of car bodies, tires, and other objects increased. Their pattern became similar to human body features, which increased FPR and decreased precision. Example results from all datasets used for evaluations are shown in In this work, we reviewed publicly available thermal datasets that could be used for person detection, and we documented the lack of diversity in these datasets. we also studied and presented a new thermal dataset and found nine different phenomena that could occur in outdoor soccer fields. The phenomena were further categorized into four categories. The impact of each category was studied for model generalization using transfer learning. Results showed that each category benefited the model generalization differently. The results showed that depending on the application, categories could be selected intelligently to obtain the desired results. The weights obtained from our dataset were further tested on three publicly available datasets. For a relatively small amount of training data from a new domain and with few iterations, good performance was achieved for person detection. Results showed that our weights could be used for model adaptation for a new domain. This will help researchers save the effort of annotating large datasets and also the time for training a new network from scratch. Moreover, with weights for YOLOv3, our new dataset is made publicly available for further research.


Data from multicenter studies reveal rates of use ranging from 20% to 64%, and MV typically lasts approximately 5 to 6 days.-5 Moreover, to use this resource, most of the time, it is necessary to resort to sedative and analgesic drugs. Therefore, it is indispensable to interrupt the MV as soon as possible.On the other hand, it is widely known that these tools can cause a variety of possible complications, such as health care-associated pneumonia, upper and lower airway injuries, and cardiovascular instability. and good medical practice fails. Currently, the duration of weaning comprises approximately 40% of the total MV time.-9When much time is spent recognizing that MV is no longer essential, the risks and costs are increased .However, defining the optimal timing for extubation is extremely important because early onset may increase morbidity and mortality, the length of stay in the PICU, and the chance of reintubation airway injury. Thus, we should consider the risk factors for extubation failure in each population with similar diseases.-13 However, in very busy PICUs, particularly those in highly complex tertiary centers, medical staff are often focused on other activities. We believe that for the patient who is already able to start the process of withdrawal from MV, the process often ends up being delayed, leading to an increased MV duration and increasing the chances of possible complications that can result.So far, decisions about starting weaning, the best time for spontaneous breathing testing and extubation are primarily doctor-centered.,14-16Thus, studies have been conducted to prove that the involvement of a multidisciplinary team (such as respiratory therapists and nurses) improves ventilatory practices and reduces the duration of MV in PICUs.-19In some countries in Europe and North America, there has been a tendency to understand the role of the multidisciplinary team in detecting “weanable” patients and in conducting the protocol.A recent Brazilian study aimed to understand the role of professionals in the PICU team during the management of patient weaning. The applied questionnaire obtained interesting data that revealed that 80% of the PICUs surveyed had dedicated respiratory therapists and that in almost 70% of the PICUs, weaning was conducted jointly between the medical team and the team of respiratory therapists. In contrast, in only 12% of the PICUs was weaning conducted by the multidisciplinary team (including nurses).This is no longer the reality found in Australia and New Zealand. Another study showed that in the PICUs analyzed, the nursing team was involved in the weaning process in up to 85% of cases and was responsible for detecting the failure of weaning in up to 94% of patients.Langue Francaise Société de Réanimation, during its 21st Consensus Conference on Intensive Care and Emergency Medicine: mechanical ventilation weaning, presented a cohort study that demonstrated that a nursing protocol for daily assessment of patients able to start weaning was effective in reducing MV times and the ICU stay. Nevertheless, another Australian study published in 2020 showed no reduction in the MV time or reintubation rates after the implementation of a nursing-driven protocol.However, this is not a new observation. In October 2001, the  In addition to the number of professionals per bed, in our opinion, there are other barriers to be overcome to achieve this ideal. We believe that for there to be an adequate multiprofessional weaning protocol, sedoanalgesia protocols must be well established as well as multiprofessional daily rounds. Family members must have a complete understanding of the process so that we can avoid family distress and suffering when progressing through the stages of awakening, the spontaneous breathing test and subsequent extubation. In general, we believe that investment in public health education is necessary to be successful in the application of this type of protocol.Those who advocate against protocols guided by other professionals in Brazil point out some of the structural aspects that hinder such protocols. Primarily, the number of professionals is an issue. The Oceania study pointed out the nurse:bed ratio of 1:1 for patients on MV, and this is not the case for Brazilian PICUs. Brazilian law requires the presence of only one nurse for every eight beds and a nursing technician for every two beds. subsequently, countless other authors reached the same conclusion.,26,27In 1996, Ely et al. proved that the simple existence of a protocol of daily evaluation of patients to verify the ability to initiate weaning was able to reduce the time of MV and even the tracheostomy rate; thus, it is part of the recommendations of the Brazilian Consensus on Mechanical Ventilation.Studies have shown that the implementation of a weaning protocol reduces its duration and, consequently, reduces ventilation in children;-32However, there is still not enough scientific evidence to support a better standardized technique for weaning. Even the Paediatric Mechanical Ventilation Consensus Conference (PEMVECC) 2017 was not fully committed to providing guidance regarding the optimal timing of initiation, the approach to weaning and the routine use of any extubation readiness testing.,33,34 and even mnemonic strategies have already been created in attempts to implement this routine.There are numerous known weaning techniques, such as the once-daily spontaneous breathing trial (SBT), multiple SBTs, gradual reduction of pressure support, gradual reduction of synchronized intermittent mandatory ventilation (SIMV) and gradual reduction of SIMV and pressure support. In adults, for approximately 20 years, daily SBT has been the most common form of ventilatory weaning, However, in our opinion, this is still not a reality in Brazilian PICUs. In our view, the strategy of reducing parameters in SIMV mode is still the strategy most commonly adopted by Brazilian pediatric intensivists.There are limited data describing ventilation and weaning practices in PICUs across the world. However, a recent European study showed that daily SBT was also the most commonly used weaning strategy in PICUsIn light of the preceding description, it is natural that we are compelled to think of strategies that are effective in reducing weaning time, reducing MV time, and therefore reducing all the consequent burdens. However, there are several factors that directly or indirectly influence this strategy, such as fluid overload, excessive sedation, delirium, malnutrition, positive end expiratory pressure, pulmonary hypertension and diaphragm function, among others.Therefore, when we think of a ventilation weaning protocol, we must have a macroscopic view and think of the patient as a complex being that is exposed to multiple different practices and with a myriad of possible responses to the proposed therapies.The same patient may fail during the weaning process due to factors related to the bedside assistant team, such as the frequency and intensity at which parameters are reduced, the timing of fitness for spontaneous breath testing, and even the criteria used for diagnosing weaning failure, which can be more permissive or more rigid.For Hartmann, a good ventilation weaning protocol must have four strong foundations to be considered robust: predetermined rules for reducing ventilator parameters; aptitude criteria for the spontaneous breathing test (SBT); a well-protocoled SBT; and well-established failure criteria.To the best of our knowledge, it has been well established that, in adults, the use of support pressure is the best way to shorten the duration of MV. Thus, in our practice, we have adopted the use of this strategy for the weaning of ventilated children in our PICUs.In our opinion, the use of support pressure does not worsen the quality indicators of ventilatory therapy and does not increase failure rates, and in addition, it shortens weaning time. Thus, we emphasize that there is no strong evidence in the literature to recommend a single strategy for pediatric ventilatory weaning. The use of supportive pressure as such a strategy is based on the opinion of these authors, which is based on their extensive clinical practice in using this ventilatory mode.There have been few studies in this area, and we believe that greater attention should be paid to this issue by pediatric MV researchers.