Furthermore, males reached significantly higher scores in each of the four subscales . There was a negative correlation between HRQoL and gonadotropin levels (FSH and fatigue r = (−)0.38, p = 0.032; FSH and emotional sphere r = (−)0.416, p = 0.018). This study found no significant associations between AddiQoL scores and the presence of autoimmune comorbidities; only fatigue scores were worse in the presence of autoimmune thyroiditis (p = 0.034). The doses of hydrocortisone and fludrocortisone in the replacement therapy were not associated with AddiQoL scores. AddiQoL scores correlated negatively with the age of diagnosis (p = 0.015). (4) Conclusions: Female sex, higher gonadotropins level, and older age at diagnosis were associated with impaired HRQoL in the studied group of patients with PAI.(1) Background: Patients with primary adrenal insufficiency (PAI) suffer from a reduced quality of life. However, clinical factors associated with this impairment remain unclear. The aim of this study was to assess the health-related quality of life (HRQoL) and to evaluate the associations with clinical and hormonal parameters in a group of patients with PAI. (2) Methods: The study included 32 patients with autoimmune PAI, who answered the quality of life in Addison’s disease questionnaire (AddiQoL). Clinical data and hormonal measurements were collected from the patients. (3) Results: The total AddiQoL score of males was significantly higher than that of females ( Primary adrenal insufficiency (PAI) is a rare chronic disease resulting from the inability of the adrenal glands to produce sufficient amounts of steroid hormones. The reported prevalence in most European countries is about 10 per 100,000 . PatientThe definition of QoL, according to the World Health Organization, includes all aspects of human life, including health, physical and social functioning, income, values, culture, spirituality, and life satisfaction. Health-related quality of life (HRQoL) is a narrower term, focusing on the effects of disease and treatment on QoL. Therefore, HRQoL cannot be compared with that of the general population but gives the possibility of a more personalized approach to the patient. A crucial principle in evaluating QoL is the patient’s perspective. Standardized tools are needed to inform the clinician about the patient’s QoL, otherwise, problems might go unrecognized. Questionnaires like Short-Form 36 (SF-36) or Checklist Individual Strength were used in patients with PAI, showing impaired QoL in comparison to that of healthy controls in spite of treatment ,6. The mThe disease-specific quality of life questionnaire in Addison’s disease (AddiQoL) was designed in 2010 by Lovas et al. and included 36 items . It was The predictors and associated factors of HRQoL in adrenal insufficiency are still unclear; however, HRQoL remains impaired irrespective of etiology . Some stSummarizing, there is a research gap in terms of the predictors of HRQoL in Addison’s disease. Therefore, the objectives of this study were to assess AddiQoL scores in a homogenous group of Polish patients with autoimmune PAI and to evaluate the associations with clinical and hormonal parameters in a prospective observational study.The study was designed as prospective and observational with assumed time of recruitment limited to two years. Therefore, all patients with a diagnosis of autoimmune PAI who were followed up in the Department of Endocrinology, Diabetes, and Isotope Therapy, Wrocław Medical University Hospital, between February 2020 and May 2022 were asked to take part in the study. The inclusion criteria were the diagnosis of autoimmune PAI confirmed at least 6 months before inclusion in the study and stable doses of hormonal replacement therapy . Only one patient during this period refused to take part in the study and was not included. During the study duration, three PAI patients in our department were diagnosed de novo, therefore, we waited 6 months to achieve the stabilization of the disease course and treatment before inclusion into the study . The samThe diagnosis of PAI was established according to the Endocrine Society Clinical Guidelines on the basis of a corticotropin stimulation test with 250 µg of synthetic ACTH. In case this test was unavailable, a morning cortisol concentration (taken at 7–8.00 a.m.) lower than 5 µg/dL accompanied with an ACTH concentration twofold of the upper reference range and typical PAI symptoms were used alternatively . HypopitEach patient was examined by an endocrinologist. The examination included taking a detailed medical history with information about current hormonal replacement therapy and mean doses for the last 6 months, as well as a physical examination including anthropometric measurements and calculation of BMI.The confounding variables in our study were autoimmune thyroiditis and diabetes as a part of autoimmune polyendocrinopathy. Seventeen females (85%) and four males (33.3%) had autoimmune thyroiditis treated with L-thyroxine supplementation. The TSH levels and L-thyroxine supplementation were stable for at least 6 months before inclusion to the study. The autoimmune diabetes treated with insulin was diagnosed in five females (25%) and one male (8.3%), the doses of insulin were stable and there were no episodes of diabetic ketoacidosis for at least 6 months before the study. A potential design bias is the single-center character of our study.p < 0.001  [The following blood laboratory tests were collected (between 7.00 and 8.00 a.m.): dehydroepiandrosterone sulfate (DHEAS), ACTH, follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol, total testosterone, thyroid-stimulating hormone (TSH), and free thyroxine (fT4). The concentrations of hormones were measured by the chemiluminescence immunoassay method . The reference ranges are presented in  weight) .Each patient completed the validated AddiQoL questionnaire with 30 questions. The validity of the AddiQoL was examined by Øksnes et al. with the use of exploratory factor analysis and Rash analysis, proving good psychometric properties and high reliability . The queItems that indicate a positive impact on QoL were scored from 1 to 6, with reverse scoring for those indicating a negative impact. Scoring was converted in the following fashion: . The algebraic sum was calculated in each patient, where a higher total score indicates a better quality of life, with a maximal result of 120 points. Points were also calculated for each patient in every aforementioned domain . All patients responded to our questionnaire. There were only 6 missing answers in all questionnaires .The Bioethics Committee of Wroclaw Medical University approved the protocol of the study (No. 115/2021). All participants gave their oral and written consent to participate in the study in accordance with the Declaration of Helsinki.t-test or Mann–Whitney test were performed to compare the quantitative variables. Correlations between parameters were calculated using Pearson’s test or Spearman’s rank correlation test. A p-value of <0.05 was considered statistically significant. The value of Cronbach’s alpha was calculated to measure scale reliability.The data were analyzed with use of Statistica software for Windows . The variables were presented as the mean with standard deviation (SD), median, and interquartile ranges (IQR). The homogeneity of the variances was determined by Levene’s test. The Shapiro–Wilk test and histograms were used to assess data distribution. Student’s The clinical characteristics of the PAI group are shown in p = 0.011). Furthermore, men reached significantly higher scores in each of the four subscales , suggesting a better HRQoL.The detailed AddiQoL score evaluation is presented in p = 0.034).No significant differences in total AddiQoL, fatigue, emotional sphere, AI symptoms, and miscellaneous scores were shown between the patients with and without diabetes. Similarly, no statistically significant differences were found in total AddiQoL and the emotional sphere, AI symptoms, and miscellaneous questions scores between the patients with and without autoimmune thyroiditis. Patients with autoimmune thyroiditis had lower fatigue scores compared to those of patients without autoimmune thyroiditis .p = 0.038; r = (−)0.4728, p = 0.015, respectively). There was a positive correlation between the emotional sphere score and body mass . There were statistically significant negative correlations between gonadotropins and AddiQoL subscales (LH and fatigue r = (−)0.384, p = 0.03; LH and emotional sphere r = (−)0.37, p = 0.037; FSH and fatigue r = (−)0.38, p = 0.032; FSH and emotional sphere r = (−)0.416, p = 0.018). We did not find statistically significant correlations between total AddiQoL, fatigue, emotional sphere, AI symptoms, and miscellaneous questions scores and age, BMI, duration of the disease, doses of hydrocortisone or fludrocortisone, UFC, ACTH, renin, TSH, and fT4.In the whole study group, total AddiQoL and fatigue scores correlated negatively with age at diagnosis (r = (−)0.4084, p = 0.046) was significant. In males, there was a negative correlation between FSH and emotional the sphere and miscellaneous questions (r = (−)0.693, p = 0.012 and r = (−)0.577, p = 0.05, respectively).We have also analyzed the correlations between testosterone, estradiol, DHEAS, gonadotropins, BMI, weight, and AddiQoL separately in males and females. In females, only the correlation between BMI and the AddiQol subscale AI symptoms . This finding is in accordance with most studies in this subject from different countries ,9,17,24,No differences in AddiQoL scores between patients on higher vs. lower doses of hydrocortisone replacement therapy were found. UFC was higher in females in comparison to that in males, but the daily doses of hydrocortisone did not differ significantly according to gender and there was also no association between AddiQoL and UFC. Therefore, based on these data, it was not possible to draw any relevant conclusion. A point of note should be made that females who had a lower BMI and received supplementation doses similar to those of males had a higher UFC level as an indirect marker of daily hydrocortisone and presented lower HRQoL. Generally, it remains impossible to assess whether a lower HRQoL is related to glucocorticoid overdosing or rather that patients who receive higher doses have a greater disease burden and therefore need higher dosing. This study group received rather high doses of hydrocortisone (mean of 26.2 mg) but presented with neither clinical nor biochemical signs of overdosing. It is possible that the study group was too small or replacement therapy too homogenous to detect such differences. While some studies reported worse HRQoL in patients receiving higher daily hydrocortisone doses , others This study found a negative correlation between gonadotropin concentration and AddiQoL scores. This suggests that menopause is another factor that impairs HRQoL in patients with PAI. It is important in everyday practice to not overlook this issue and possibly implement sex-steroid replacement therapy in females with PAI, whose well-being is impaired due to chronic disease and who are at risk of autoimmune premature ovarian failure.There was no difference in AddiQoL scores between patients with and without other autoimmune diseases (diabetes and autoimmune thyroiditis) in this study group, with the exception of fatigue correlating with the presence of autoimmune thyroiditis. Other studies found that the more autoimmune comorbidities re present, the worse the HRQoL scores are . Celiac In this study, total AddiQoL and fatigue scores correlated negatively with age at diagnosis, in accordance with the results of Meyer et al. . In a NoOur study has some limitations. The most important one is the number of included patients, which is mostly the result of the rarity of the disease as well as the limited time of the patient enrollment phase. It was also a single-center study, which may have caused some bias. Additionally, the impact of other conventional glucocorticoid treatment regimens  could not be included due to the lack of availability of such preparations in Poland. The use of AddiQoL, which is a specific questionnaire designed for PAI, is both a limitation and a strength of this study. The AddiQoL is sensitive enough to evaluate the HRQoL in PAI because it is adjusted to the typical symptomatology and the course of the disease and enables a more personalized approach to the patient. A high value of Cronbach’s alpha indicates internal consistency of the scale. On the other hand, it is not possible to compare the AddiQoL results with those of a healthy population. Another strength of this study is the complexity of the clinical and hormonal assessment and the autoimmune cause of PAI in all patients. However, HRQoL is not a stable parameter and may change in the course of the disease and due to other psychosocial factors, therefore, longitudinal data are needed in order to assess whether different interventions in PAI may prove some benefit in HRQoL. It is also important to take a more holistic approach in relation to the biopsychosocial model. Thus, it is crucial to take into account the quality of life and the patient’s psychological resources and limitations, as well as aspects of social health, which seem to be of great importance in chronic somatic diseases. Therefore, there is a need for further multicenter studies with a higher number of participants that would investigate the predictors of HRQoL in PAI, especially during a longitudinal observation. Additionally, to assess the impact of androgen supplementation on HRQoL, intervention studies are needed with larger groups of participants, because so far, only inconsistent data are reported in this area.Our study brings some practical suggestions for physicians, especially in terms of the importance of estradiol replacement therapy in menopausal females of older age at PAI diagnosis, because in such patients, the HRQoL might be particularly impaired.In conclusion, in this prospective observational study comprising 32 patients with PAI, HRQoL was assessed with a specific questionnaire (AddiQoL) and was significantly worse in females than in males. HRQoL impairment was associated with higher levels of gonadotropins and younger age at diagnosis. There was no association between HRQoL and hormonal replacement therapy regimen. This subject needs further studies, especially with a longer follow-up.


The co-occurrence of substance use disorder and mental disorder, known as dual diagnosis, has a distressingly high prevalence among individuals grappling with either of these conditions. Mood disorders, especially depression, constitute a substantial burden for People Who Inject Drugs (PWID) and a significant public health concern in Vietnam. Identifying risk factors for depression in PWID is imperative for the development of targeted interventions.We enrolled PWID into a cohort using the respondent-driven sampling method. Over a 36-month period, we systematically tracked the emergence of depression and employed multiple imputation in conjunction with a mixed nonlinear model to pinpoint risk factors for depression in this demographic. At inclusion, depression was screened using the PHQ-2 questionnaire, and subsequent episodes of depression were assessed semi-annually using the CES-D8.Three hundred and ninety-one PWID (26.6%) were depressed. Major risk factors for depression included being female, not having a permanent residency, having been hospitalized and using methamphetamine more than weekly. Other risk factors included age, being single, not having a health insurance card and not being on methadone.The exclusion of missing visits and social desirability could have led to selection and information biases. In this observational study, confusion biases are possible despite our best efforts.Depression is alarmingly frequent in PWID. In this study taking in account the chronological relationship between sociodemographic and clinical factors and depression, risk factors were identified in this specific setting of low-to-middle income country. The findings highlight the need to develop innovative targeted psychiatric interventions with the help of supporting peers. HoweverThe most common psychiatric condition in patients with SUD are mood disorders, with prevalence rates varying from 17% in Canada to a broader range of 27 to 85% in Australia, as reported in a meta-analysis . More spIn Vietnam, as in numerous low- to middle-income countries (LMICs), psychiatric resources are sparse, resulting in a saturated mental health system  managingIn this context, targeting specifically people who inject drugs (PWID) who are in need of mental health support is therefore crucial for they present, among other mental health issues, significantly higher rates of depression . PreviouThe aim of our project is to develop a community-based innovative intervention plan to prevent and limit the burden of depression in this specific population. In order to do so, we aimed in this study to explore longitudinal associations between depressive symptoms and patient characteristics in people who inject drugs in Hai Phong, Vietnam.2.2.1.via community-based respondent-driven sampling (RDS) surveys combined with linkage to HIV care. The RDS method allows access to hard-to-reach populations, hidden from care and inaccessible through classic recruitment methods (The Drug use and Infections in Vietnam (DRIVE) program is an international project launched in 2016 whose goal was to end the HIV epidemic among PWID in Hai Phong, Vietnam . The DRI methods .Three rounds of annual RDS surveys from November 2016 to November 2018 targeted 3,150 PWID. The inclusion criteria were being at least 18 years old, having a positive urine test for heroin or methamphetamine and having skin marks from a recent injection . Those uStarting from the first RDS survey, two open cohorts were created: one with HIV-negative participants to estimate HIV incidence and another with HIV-positive participants to estimate the cascade of HIV care dynamics. All RDS HIV-positive participants were invited to be included in the cohort and were then provided counseling and peer support to help them connect with care and initiate or remain on antiretroviral treatment (ART) as well as encourage them to pursue methadone maintenance therapies (MMTs). HIV-negative participants were recruited until the target cohort size was reached, which was set to 400 for RDS1 and 400 for the next two RDS rounds, excluding those who reported already being treated with methadone . Partici2.2.In this study, sociodemographic, drug habit, psychological, behavioral and health event-related data from all PWID recruited in the previously described HIV+ and HIV- cohorts and who participated in at least one follow up visit were included.2.3.2.3.1.At baseline (RDS survey), depression was screened with a short form of the Patient Health Questionnaire (PHQ-2) , in faceDuring semiannual follow-up visits, depression was measured by trained doctors from the Hai Phong University of Medicine and Pharmacy using a short form of the Center for Epidemiologic Studies- Depression (CES-D8) questionnaire , shown t2.3.2.Participants were screened for anxiety at the time of the RDS survey with the short Generalized Anxiety Disorder questionnaire (GAD-2) developed to screen for anxiety in primary care patients and the general population, with a published cutoff of ≥3 .Participants who screened positive for any mental health issue during the RDS survey or during follow-up were invited to meet with a psychiatrist for a full clinical assessment followed by other consultations when necessary at the study site.2.3.3.Data were collected by trained interviewers (CBO members) using a face-to-face questionnaire on drug and alcohol use. The questionnaire created for the study included questions on duration, age of initiation, frequency and substances injected, as well as on the use of other non-injected substances including alcohol. Its content has been described in greater detail elsewhere , 33. PWIhigh heroin injection frequency referred to PWID reporting more than 30 injections in the past month. Methamphetamine use had two categories, “no use or less than once per week” and “more than once per week”  in addition to heroin, which was used by all participants.A er week” . We combAlcohol misuse was assessed through Audit-C, a 3-item questionnaire including the first questions of the AUDIT questionnaire, with a cutoff of ≥3 for women and ≥ 4 for men , as well as social and structural sociodemographic measures . PWID were also asked if they had been hospitalized during the past 6 months, whatever the reason, and were tested for HCV and HIV at every visit.At the RDS visit, CBO members collected individual sociodemographic measures: 2.4.gender, age, relationship status, stable housing and health insurance card), drug habits , and health events . Anxiety was not entered in the model as cofounder because of anxiety-related questions within the CES-D scale   data for participants who had completed their follow-up visits, we selected complete outcome cases and used a multiple imputation technique: the model was apply to 20 datasets of possible values created ls (CIs) .The data analysis for this paper was performed using SAS software version 9.4. Copyright © 2013 SAS Institute Inc.2.5.The DRIVE study was approved by the Institutional Review Boards of the Hai Phong University of Medicine and Pharmacy, the Icahn School of Medicine at Mount Sinai, and the New York University School of Medicine. All participants provided signed informed consent.3.3.1.Of the 1,691 people included in the DRIVE cohort, 215 never returned for any follow-up visits, 2 died. and 3 began antidepressant treatment before the first follow-up. In total, 1,471 people were included in our study, 772 from the HIV-negative cohort and 699 from the HIV-positive cohort . They we3.2.All participants were heroin users, 93% were male, and their mean age was 41 years . OverallOf our 6,768 planned follow-up visits, 5,201 (76.8%) were completed. During follow-up, 56 people had died, including 1 from suicide, 2 from overdoses and 2 for unknown reasons. Other causes of death had no direct link with depression. 394 (5.8%) visits were attended but had incomplete data on the CES-D8 questionnaire.The 329 participants who dropped out (22%) were younger, were less likely to be in a relationship or in a stable housing, were more depressed at inclusion, and were more likely to use methamphetamine more than once per week. They were less likely to be enrolled in methadone maintenance therapy and less likely to be infected by HIV .3.3.The proportion of depressed participants was 11.9% at baseline. The number of PWID depressed at one or more visits was 391 (26.6%). Among people free from depression in the 2 weeks prior to inclusion based on the PHQ-2, 320 (23.8%) developed depression over the mean 16 months of follow-up.3.4.No permanent residency , gender , having been hospitalized and using methamphetamine at least once a week , not being in relationship nor on methadone , not having an insurance card , age  were all significant risk factors for depression. Time since inclusion in the cohort was a protective factor against depression . Being infected with HCV tended to be associated with depression without statistically significant .4.4.1.In this cohort of PWID recruited through RDS surveys, almost a quarter had depressive symptoms develop or reoccur during a follow-up of 16 months on average. This is more than previously found in a Canadian open cohort of PWID  but loweThe gender gap in depression has been extensively studied in the general population and could be attributed to various factors. Hormonal fluctuations , 45, difAnother potential explanation could be gender disparities in involvement in sex work. While we could not establish gender differences in sex work due to underreported or unanswered questions in this study, another article from the DRIVE project, focusing on a similar population, found that being a woman was linked to engagement in sex work , a knownOther factors reflecting social precariousness emerged. “Living on the edge” without stable housing or a health insurance card has already been identified as a risk factor for mental illness . The conThe prevalence of depression increases with age in both the general population and PWID , 63. ThiIndeed, having been hospitalized during the follow-up increased the risk of depression. Despite the fact that we had no information on the causes of these hospital admissions, they might be related to complications or treatments of infections, which are frequent in our cohort population, as well as a degraded state of health, commonly associated with depression .We found that HCV had a tendency to increase the risk of depression, although not significantly. In literature, this observed association  has beenGenerally, we observed that time since inclusion was a protective factor. Our patients participated in a research program offering them the opportunities to be tested, examined and treated. Furthermore, the positive social impact of being enrolled in a cohort and going to regular meetings with CBO-trained members for social support, harm reduction and social and administrative counseling might have helped reduce the risk of depression. Being part of a community also offers, in the context of layered drug- and HIV-related stigma, the possibility to disclose their situation to someone .4.2.In many countries, where access to psychiatric care is notably limited, alternative and original strategies that rely on nonprofessional or peer workers could be crucial. Using simple screening tools in the community to introduce drug users to their mental health may be the first step.in situ is proposed. Screening followed by delocalized psychiatric consultation is one of the innovative approaches that allows the care and monitoring of hard-to-reach vulnerable populations.A simplified psychiatric screening tool adapted to nonprofessional/CBO members was developed as part of the DRIVE project. This 9-item questionnaire validated among PWID with acceptable psychometric characteristics and good acceptability , 75 alloPsychiatric complications, particularly depression but also psychotic manifestations, are very common among frequent methamphetamine users and need special attention and tailored harm reduction interventions . AdaptedEnrollment in MMT should be encouraged, since having the easiest possible access to MMT was found to be a protective factor , 79–81. It has become necessary to promote the development of psychiatric interventions in environments dedicated to drug users (methadone or HIV clinics) . In this4.3.First, owing to limitations in resources, staffing, and organizational capacity, we were unable to conduct psychiatric evaluations based on international criteria. The PHQ-2 used at baseline has a sensitivity of 72% , meaningSecond, we had to restrict our data to people with at least one follow-up visit; some were lost to follow-up, and we considered only visits with completed outcomes. This could induce some selection biases because it is plausible that people with high depression risk are the least able to visit, meaning that the more severe cases of depression were not as represented as others. We observed this phenomenon when we compared people with missed or excluded visits with the rest; the former presented more risk factors for depression.Third, sampling biases could occur because our recruitment method included a large majority of male PWID. Our findings are representative of Vietnam and South Asia  but mighFinally, avoided sensitive questions and missing data could be explained by desirability biases. For instance, questions about heroin experience and sex work were often not answered. Recall bias could also have occurred since questions were related to past behaviors.5.The co-occurrence of addiction and depression is a scourge with catastrophic consequences for individuals affected by it. In this large longitudinal study focusing on people who inject drugs in a low-to-middle income country setting, we found a concerning high prevalence of depression.This work was able for the first time in this setting to consider the temporal relationships of the factors studied and their impact on depressive symptomatology. We found that being a woman was strongly associated with depression. Certain factors of social precariousness were also identified as risk factors, as well as the consumption of methamphetamine in addition to substance injection. The beneficial effect of being on a MMT program was also emphasized.All these results drive, in the context of limited psychiatric resources, the development of innovative interventions focusing on these vulnerable sub-populations, such as simple screening tools, delocalized psychiatric consultations, peer support, free treatment, etc.… Other interventions remain to be designed and tested in these specific settings.The original contributions presented in the study are included in the article/The studies involving humans were approved by the Institutional Review Board of the Hai Phong University of Medicine and Pharmacy, the Institutional Review Board of the Icahn School of Medicine at Mount Sinai, and the Institutional Review Board of the New York University School of Medicine. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.LiM: conceptualization, methodology, software, formal analysis, writing–original draft, and visualization. SL: investigation, validation, and writing–review and editing. VH: investigation and writing–review and editing. DH and DD: funding acquisition, supervision, and writing–review and editing. KM and J-PM: resources, supervision, and writing–review and editing. KO: funding acquisition and writing–review and editing. DR and CQ: resources, supervision, data curation, and writing–review and editing. TT: supervision and writing–review and editing. RV: data curation and writing–review and editing. GH: supervision, data curation, and writing–review and editing. DL and JF: writing–review and editing. LaM: supervision, conceptualization, methodology, and writing–review and editing. NN: funding acquisition, supervision, conceptualization, methodology, and writing–review and editing. All authors contributed to the article and approved the submitted version.


Arrhythmia is a cardiac condition characterized by an irregular heart rhythm that hinders the proper circulation of blood, posing a severe risk to individuals’ lives. Globally, arrhythmias are recognized as a significant health concern, accounting for nearly 12 percent of all deaths. As a result, there has been a growing focus on utilizing artificial intelligence for the detection and classification of abnormal heartbeats. In recent years, self-operated heartbeat detection research has gained popularity due to its cost-effectiveness and potential for expediting therapy for individuals at risk of arrhythmias. However, building an efficient automatic heartbeat monitoring approach for arrhythmia identification and classification comes with several significant challenges. These challenges include addressing issues related to data quality, determining the range for heart rate segmentation, managing data imbalance difficulties, handling intra- and inter-patient variations, distinguishing supraventricular irregular heartbeats from regular heartbeats, and ensuring model interpretability. In this study, we propose the Reseek-Arrhythmia model, which leverages deep learning techniques to automatically detect and classify heart arrhythmia diseases. The model combines different convolutional blocks and identity blocks, along with essential components such as convolution layers, batch normalization layers, and activation layers. To train and evaluate the model, we utilized the MIT-BIH and PTB datasets. Remarkably, the proposed model achieves outstanding performance with an accuracy of 99.35% and 93.50% and an acceptable loss of 0.688 and 0.2564, respectively. Heart disease (HD) is a lethal condition affecting a large number of human beings around the globe when compared to other diseases. According to the World Health Organization (WHO), HD is the leading cause of death globally, and it is estimated that HD is responsible for 17.9 million deaths per year ; and (ivArrhythmias can be diagnosed with the help of an electrocardiogram (ECG). The ECG is a well-established tool in cardiology for monitoring a patient’s heart condition. The 12-lead ECG is the most effective equipment for recording cardiac rhythm. The leads are the recording channels, and they are lead I, lead II, lead III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6. Every channel records from a distinct perspective. Every normal ECG heartbeat has three main waves, namely the P-wave, T-wave, and R-point, as shown in Arrhythmia HD refers to abnormal heart rhythms, requiring trained professionals to visually analyze complex and diverse ECG data. This process can be time-consuming and costly. Accurate identification of arrhythmia beats is challenging due to variations in their forms and patterns. Extracting valuable insights from complex medical data records remains a significant challenge in the medical field. To overcome the challenges, several techniques are used for automatic detection and classification ,4,5,6.Computer vision, artificial intelligence (AI), and its subsets, machine learning (ML) and deep learning (DL), are used for automatic arrhythmia detection and classification. Recent deep learning advancements offer practical frameworks for comprehensive models with high-dimensional data. AI has gained astounding success in detecting and discriminating high-level images in recent years. In 2012, for instance, a deep learning model called AlexNet, which was composed of a convolutional neural network (CNN)  model, aProposes Reseek-Arrhythmia model to detect and classify arrhythmia HD.Utilizes two different datasets, namely the MIT-BIH and PTB datasets, to evaluate the proposed model.Handles unbalanced and noisy data.Evaluates the proposed model using various performance metrics, including accuracy, precision, recall, F1-score, and loss.Compares the performance of the proposed model with previous studies in the field.The main contributions of this paper are as follows:Numerous studies have been undertaken regarding the identification and classification of arrhythmias. The purpose of  is to inIn this study, the performance of neural networks in identifying electrocardiogram (ECG) patterns as normal or abnormal is investigated. With 91.9% accuracy in binary classification and 75.7% accuracy in multi-class classification, the neural network (NN) model, including support vector machines (SVMs), random forests, and logistic regression, outperforms. When compared to CPUs, the utilization of GPUs dramatically speeds the training process. The subject of this work is cardiac dysrhythmia, which kills approximately 500,000 people in the United States each year .Former research utilized the multi-layer neural network approach for multi-class and binary detection . Inverse and stochastic gradient descent were used in the approach; the greater the number of neurons employed by the researcher, the lower the accuracy. As a result, the number of neurons must be adjusted based on the data. For the conclusion, the author utilized two hidden layers with 50 nodes each. A GPU booster was utilized to shorten the learning time. As a consequence, the neural network could now function six times quicker. The accuracy on binary classes was 91.9 percent and 75.7 percent on multi-class. This method used the deep belief network (DBN) and deep learning research to detect arrhythmia. ECG data were divided into six categories, including normal data, while SVM and ANN were also used for detection and DBN for the extraction of the features. The Gaussian kernel SVM achieved the best accuracy of 95.49 percent . AnotherThe second strategy used “stacked auto-encoders” and “restricted Boltzmann” machines to develop different attributes of training. The former method had an accuracy of 88.45 percent, while the latter had an accuracy of 90.64 percent . This stThis study describes a unique method for classifying cardiac arrhythmias based on ECG data. Using different approaches, including CNNs, the proposed method integrates several characteristics retrieved from the ECG data. Morphological, chronological, and statistical characteristics are among the features. A composite feature vector is produced and fed into a CNN classifier for label prediction. On the MIT-BIH database, the system obtains an average classification accuracy of 98% . They us(1)This study may be used to increase the prediction ability of models that take cardiac abnormalities into account, and it can be used for datasets that are both broad and varied.(2)Real-time monitoring of cardiac patients is necessary for the development of effective algorithms, the extraction of features, and categorization.(3)This study employs complex categorization methods. These models complement the MPA algorithm to create meaningful classification outputs with increased accuracy, and they have the potential to improve classification process accuracy.(4)The findings corroborate previous research. Most existing techniques are slower and less accurate than the MPA-CNN method. MPA with CNN classifier had detection precision levels of 99.31 percent (MIT-BIH), 99.76 percent (EDB), and 99.47 percent (EDB) (INCART).Prior studies examined the effectiveness of the categorization method for predicting heart disease using LR and back-propagation neural networks (BFNNs). In order to categorize cardiac disease, they compared parametric and non-parametric techniques. They obtained a private dataset, which has 270 records and 13 attributes, 11 of which are used to support their research. Using a 10-fold cross-validation method, the experimenters determined the estimation of their detection models’ objectivity. According to their findings, LR obtained 91 percent accuracy, and BFNN achieved 88 percent accuracy . In 44]44], theyTo address the limitations of traditional machine learning approaches, a new approach was developed that uses a multi-layered dilated CNN and a bidirectional RNN to identify arrhythmia signals in ECG recordings. The multi-layered dilated CNN layer is a shallow convolutional layer that extracts features from ECG data. It does this by taking the data as input. The researchers were able to achieve the degree of precision that was anticipated. Traditional machine learning no longer has to rely on feature extraction, which may take a significant amount of time and require a substantial amount of prior biological knowledge.The heart illness arrhythmia, sometimes referred to as unstable heartbeats, is considered a major health concern. According to statistics, around 12% of the world’s population dies from this ever-growing disease or series of disorders annually . It occui.Divide the dataset into two sets: training (70%) and testing (30%).ii.Apply cleaning techniques.iii.Apply data augmentation techniques.iv.Design the proposed Reseek-Arrhythmia model for detecting and classifying heart arrhythmia disease.In this research, we present an approach for detecting and classifying heart arrhythmia disease from a different dataset, which is an extremely unbalanced dataset. The suggested approach is separated into four main phases:To train the proposed model, two different datasets, namely PTB and MIT-BIH, were utilized to detect and classify cardiac arrhythmia disease.https://www.physionet.org/content/ptbdb/1.0.0/ accessed on 4 August 2023) is a publicly available dataset widely used in ECG analysis and arrhythmia detection research. This dataset provides a rich source of annotated ECG recordings from a diverse patient population with various cardiac conditions, including arrhythmia. Including recordings from healthy individuals also allows for assessing the specificity of deep learning models for arrhythmia detection. The dataset is annotated with diagnosis codes based on the Minnesota Code, which provides a standardized coding system for ECG diagnoses and facilitates the comparison of results across different studies. The dataset consists of 14,552 ECG beats, of which 4046 are classified as normal and 10,506 as abnormal.The PTB Diagnostic ECG Database (https://www.physionet.org/content/mitdb/1.0.0/ accessed on 4 August 2023) is the collection of ECGs used for arrhythmia detection and classification. The MIT-BIH arrhythmia dataset comprises 109,446 ECG beats with a sampling frequency of 125 Hz, presented in a CSV file format. The dataset contains 87,554 ECG beats for training and 21,892 ECG beats for testing. Then, the dataset is classified by categorizing the ECG beats into five distinct classes: N, S, V, F, and Q. These classes are defined in The publicly available MIT-BIH dataset . Subsequently, to combat overfitting issues, this study used data augmentation techniques. Moreover, different techniques such as random sampling, class augmentation, and normalization were used throughout the data augmentation process. Then, a process known as min–max normalization was carried out, which rescaled the values to fall somewhere between 0 and 1. The following is the normalization formula:The rescaling method was used to save computational resources and make the data easy to understand. Then, we finally proposed a Reseek-Arrhythmia model, which is discussed in the The fundamental purpose of this study is to provide a solution for the detection and classification of heart arrhythmia disease. The dataset that is used in this study is extremely unbalanced, as shown in Python: The Python programming language was the core language used for developing the model due to its versatility and strong support for machine learning frameworks.TensorFlow: TensorFlow, an open-source machine learning framework developed by Google, was employed for building and training the deep learning model.Keras: Keras, a high-level neural network API written in Python, was used as an interface for building and configuring the neural network layers.NumPy: NumPy, a fundamental package for numerical computations in Python, was employed for handling mathematical operations and data manipulation.Pandas: The Pandas library was used for data preprocessing and manipulation, facilitating efficient handling of datasets and data frames.Matplotlib and Seaborn: These libraries were utilized for data visualization, aiding in the creation of various graphs and plots to present the model’s performance.Scikit-learn: Scikit-learn, a machine learning library for Python, provided essential tools for evaluating the model’s performance and implementing machine learning algorithms.Jupyter Notebook: Jupyter Notebook was used as the interactive coding environment, enabling code execution, data exploration, and result visualization in an integrated manner.The experiments were performed on Dell computers  configured with a Core i5 CPU, 8 GB of RAM, and Windows 10. The software environment and packages utilized during the development of the proposed Reseek-Arrhythmia model include:These software components and packages collectively provided a robust foundation for the development, training, and evaluation of the Reseek-Arrhythmia model. The combination of these tools facilitated efficient implementation and analysis of the deep learning techniques applied in this study. Each of the convolutional blocks and identity blocks will be discussed in the Another sort of building block that is often utilized in CNNs is the convolutional block . It is mAn identity block is a sort of residual block that enables a neural network to bypass specific layers. The input to an identity block is appended straight to the output of the block, allowing the network to “skip over” the block if further processing of the input is not required. An identity block is made up of a series of convolutional layers, followed by batch normalization and activation layers, and finally the addition of the input and output tensors. The identity block is intended to help prevent the problems of vanishing and gradients that can arise while training deep neural networks. It enables the network to learn more efficiently by allowing direct information flow via a shortcut connection between them. The identity block improves the flow of information from one layer to the next by keeping the input and output tensors in the same dimension, where each of the identity blocks consists of multiple layers such as convolution, batch normalization, and activation layers, as can be observed in To evaluate the effectiveness of the proposed Reseek-Arrhythmia model in classifying arrhythmia diseases, several evaluation metrics were utilized. The Adam optimizer with a learning rate (Lr) of 0.001, beta1 of 0.9, and beta2 of 0.999 was employed to optimize the model for achieving the best possible solution. The beta1 and beta2 parameters control the decay rate of the first and second moments of the gradient and accept values between 0 and 1 .Confusion metrics were used to evaluate the outcome parameters, including accuracy (ACC), recall, precision, F1-score, and loss. These metrics provide a comprehensive understanding of the model’s performance. In the context of confusion metrics, “true positives” (TP) refer to observations that are both expected and actual positives, while “false positives” (FP) occur when an observation is expected to be positive but turns out to be negative. “True negatives” (TN) are observations that are projected to be negative and turn out to be negative, and “false negatives” (FN) are instances where performance indicators are incorrectly labeled as negatives.The proposed Reseek-Arrhythmia model incorporates various performance metrics to assess its classification capabilities. Accuracy measures the overall correctness of the model’s predictions, while precision evaluates the model’s ability to accurately identify positive instances. Recall quantifies the model’s capacity to identify all positive instances, and the F1-score combines precision and recall into a single metric. Additionally, the loss is used as a performance indicator that measures the deviation between the model’s predicted values and the actual labels. By employing these performance metrics, the proposed model’s effectiveness in detecting and classifying arrhythmias is thoroughly evaluated. The inclusion of these metrics enables comparisons with previous approaches and demonstrates the superior performance of the Reseek-Arrhythmia model in accurately identifying and classifying arrhythmia diseases.ACC [The accuracy metric, denoted as ACC , is a coTP) and true negative (TN) predictions. The red line represents the accuracy of the training data, while the blue line represents the accuracy of the validation data. Accuracy provides an overall measure of how well the model performs in classifying instances correctly, and it has been proven that the proposed model is able to successfully identify and classify arrhythmia patterns with a high level of accuracy.Specificity  is a staTN) instances, indicating how well it distinguishes normal, non-arrhythmic patterns. The high specificity scores achieved by the model demonstrate its proficiency in accurately classifying non-arrhythmic instances, further solidifying its effectiveness in arrhythmia detection and classification tasks.Sensitivity , also knTP) instances, indicating its effectiveness in detecting and classifying arrhythmic patterns. The high sensitivity scores attained by the model indicate its strong ability to accurately identify instances of arrhythmias, making it a valuable tool for the early detection and classification of cardiac arrhythmia disease.The proposed model demonstrated remarkable sensitivity scores on both the MIT-BIH and PTB datasets, achieving 98.59% and 99.55%, respectively. TP) cases among all positive predictions. Precision is calculated by dividing the number of TP predictions by the sum of TP and FP predictions, as shown in Equation (5):Precision  is anothPrecision is a crucial metric that provides valuable insights into the model’s accuracy in identifying specific types of arrhythmias. A high precision score, close to 1, indicates that the model has a low rate of false positive predictions, meaning that it accurately classifies positive instances with a high level of precision. A low accuracy score closer to 0 indicates that the model has a larger rate of false positives, which indicates a less accurate categorization for positive cases. The proposed model achieved precision of 98.39% and 92.97% on the MIT-BIH and PTB datasets, respectively, as shown in FN) predictions, as shown in Equation (6):On the other hand, recall measures the model’s effectiveness in correctly identifying positive cases out of all the actual positive cases in the dataset. In the context of evaluating a deep CNN model’s ability to recognize positive classifications in real-world scenarios, the recall metric  is employed. The recall value ranges from 0 to 1, representing the model’s ability to identify positive instances. A higher value means better performance in recognizing true positives. The recall is calculated by dividing the number of true positive predictions by the sum of true positive predictions and false negative . By achieving low loss values on both datasets, the proposed model demonstrates its ability to efficiently identify and classify heart arrhythmia disease patterns with high accuracy, precision, recall, and minimal errors. This study addresses the challenges associated with manually analyzing electrocardiogram (ECG) records, which include time-consuming procedures and potential inaccuracies due to irregularly occurring arrhythmias. The focus of the article is on developing an automated method using deep learning techniques to assist physicians in identifying and categorizing arrhythmia disorders. The suggested methodology consists of four major steps. Firstly, split the dataset into training and testing sets, which allows the model to learn patterns and make predictions on unseen data. Next, cleaning techniques are applied to remove any unwanted data that may hinder the performance of the model. Data augmentation techniques are then used to combat overfitting, which is a phenomenon where the model becomes too specialized for the training data and performs poorly on new data.Preprocessing techniques are applied to make the data more manageable. This includes rescaling the data using min–max normalization, which transforms the values into a specific range to ensure consistent processing. The proposed model for this study is called the Reseek-Arrhythmia model. It is designed to handle the highly imbalanced dataset used in the study, meaning that certain arrhythmia types may have significantly fewer examples compared to others. The Reseek model is made up of the convolution block and the identity block, where the convolutional blocks have the responsibility of extracting features from the input data that are necessary for analyzing the patterns and attributes of various arrhythmias. In order to overcome the issue of vanishing gradients, which can impede learning in DNNs, the identity blocks enable information to flow across short-cut connections.To assess the efficiency of the proposed approach, two publicly accessible datasets are utilized: the PTB Diagnostic ECG Database and the MIT-BIH dataset. The MIT-BIH dataset contains a significant number of ECG beats categorized into five arrhythmia classes: N, S, V, F, and Q, whereas the PTB dataset is classified into two classes: normal and abnormal. The suggested model’s performance is measured using a variety of parameters, including accuracy, sensitivity, specificity, precision, recall, and loss. Accuracy assesses the model’s overall accuracy of predictions, whereas precision and recall assess the model’s capacity to properly identify positive cases and cover all relevant examples, respectively. Loss is a statistic that measures the difference between the model’s predicted value and the actual value. This study intends to give insights into the usefulness of the suggested automated method for recognizing and categorizing arrhythmias using DL techniques by analyzing the model’s performance using these parameters. The study’s outcome emphasizes the relevance of arrhythmia as a cardiovascular disease that affects a huge population worldwide. Early detection and accurate diagnosis of arrhythmias are crucial for effective therapy and management of the condition. Deep learning strategies have gained popularity in recent years for their potential in the early identification and classification of arrhythmias. This study focuses on utilizing electrocardiogram (ECG) data to develop practical deep-learning approaches and create an efficient model for classifying cardiovascular illnesses. The proposed architecture in this study combines different convolutional blocks and identity blocks, employing the MIT-BIH and PTB datasets. These convolutional blocks analyze various features and patterns present in the data, enabling the model to learn the distinctive characteristics associated with different arrhythmias. On the other hand, identity blocks are employed to address the problem of vanishing gradients, which can occur during the training of deep neural networks. The identity blocks establish shortcut connections that allow direct information flow between layers, mitigating the vanishing gradient issue. This facilitates more efficient learning and enhances the overall performance of the model. The study reports impressive results after training the proposed model using the combined datasets. The proposed model outperformed with an accuracy of 99.35% and 93.50%, showing that it properly diagnosed arrhythmias in the majority of instances. The model also obtained a low loss value of 0.688 and 0.2564, which assesses the difference between predicted and categorized actual values. The proposed model’s efficacy demonstrates its potential to aid healthcare practitioners in the early diagnosis and classification of arrhythmias.


Using Antimicrobial stewardship programs (ASP) to monitor the use of antibiotics can lead to improved antibiotic use and reduced costs.This retrospective cohort study was done at Shiraz Organ Transplant Center, the largest transplant center in Asia. Antimicrobial use, cost, clinical outcomes, and antibiotic resistance pattern were evaluated before and after ASP.p = 0.04). In addition, the overall cost of antibiotics purchased was much lower after the ASP measures were implemented ($43.10 per PD) than before implementation of the ASP measures ($60.60 per PD) (p = 0.03). After the implementation of ASP, the number of MDR isolates was significantly reduced.This study included 2791 patients, 1154 of whom were related to the time before ASP and 1637 to the time after ASP. During the period of the research, a total of 4051 interventions were done. The use of all classes of antibiotics was significantly reduced by ASP, with 329 DDD/100PD before the intervention compared to 201 DDD/100PD after it (The results of our study showed that the implementation of ASP significantly reduced the number and costs of antibiotics and also the number of resistant pathogens, but did not affect the patients’ length of stay.The online version contains supplementary material available at 10.1186/s12893-023-01991-y. Solid-organ transplantation (SOT) is the best-known therapeutic option for numerous end-stage diseases in the acute and chronic stages. It has been associated with improved survival and enhanced quality of life for patients , 2. In rThis retrospective single-center cohort study was conducted from March 2020 to November 2021 at Shiraz Organ Transplant Center, Shiraz, Iran which is affiliated to Shiraz University of Medical Sciences, the largest transplant center in Asia, which has 350 beds and performs over 600 solid organ transplants including the liver, kidney, pancreas-kidney, intestine, heart, and lungs, every year. At this center, all medical services are provided to End-Stage Renal Disease (ESRD) and end-stage liver and organ failure disease patients before and after transplantation. This study was approved by the Institutional Review Board and the Ethics Committee of Shiraz University of Medical Sciences . All of the protocols were based on the ethical guidelines of the 1975 Helsinki Declaration .Before performing ASP (From March 2020 to December 2020 is related to the pre-ASP period) in the hospital, transplant surgeon specialists, gastroenterologists, or nephrologists prescribed antibiotics  based on clinical or laboratory evidence, and in cases of necessity, they consulted infectious disease specialists.The ASP was established and implemented in the hospital in December 2020 . An infectious disease specialist, a clinical pharmacist, a skilled infectious disease nurse, a clinical microbiologist, and one of the organ transplant team surgeons were among the team members. Since 2020, annually, the resistance-susceptibility pattern of isolated pathogens of the hospital has been prepared separately for each ward and different classes of antibiotics by the antibiotic stewardship team have been the basis for the selection of antibiotics in each ward. Furthermore, based on the trials conducted , local gAll demographic, clinical, laboratory, and follow-up information of hospitalized patients during the whole study period was extracted from the hospital HIS system and electronic patient records. Also, all the interventions performed by the team were carefully recorded. The World Health Organization (WHO) Collaborating Center for Drug Statistics Methodology advised that antibiotic use should be standardized using the Anatomical Therapeutic Chemical (ATC) classification system and the DDD, as a measuring unit. The amount of antibiotics consumed by inpatients was measured in DDD/100 bed-days . All theAcinetobacter baumannii, Escherichia coli, Klebsiella pneumonia, Pseudomonas aeruginosa, Methicillin-resistant Staphylococcus aureus, and vancomycin-resistant enterococci (VRE).The primary aim of the study was to compare antimicrobial use data expressed as DDD before and after the ASP. A secondary goal in our institute study included comparing the total cost of antibiotics as well as changes in antibiotic resistance patterns, with a focus on the (MDR) pathogens such as P < 0.05 were considered significant.SPSS software version 16 was used for all statistical analyses. The Smirnov-Kolmogorov test was used to determine whether the data were normally distributed, and statistical analysis was performed using t-test, Mann–Whitney test, or Wilcoxon test, depending on the distribution. p = 0.04), was seen. Surgical wards have had a much higher reduction in the use of all classes of antibiotics than medical wards (p = 0.01). The clinical outcomes associated with ASP interventions are described in Table p = 0.03). The prevalence of different microorganisms before and after the ASP intervention is shown in Fig. Two thousand seven hundred ninety-one patients participated in this study; they were waiting for transplantation or had undergone solid organ transplantation; of them, 1154 patients were related to the time before ASP and 1637 to the time after ASP. The mean age of this group of patients was 54.31 ± 13.21 years. The demographic information of the patients is shown in Table In comparison between the patients waiting for transplantation and transplanted ones, the results of our study showed that the percentage of reduction in the usage of third generation cephalosporins (-13.90% vs. -18.92%), carbapenems.p < 0.001).(-2.00% vs. -4.01%) and linezolid (-28.80% vs. -32.00%) in transplant patients was significantly lower than those waiting for transplant after ASP implementation ; after performing ASP, the mean duration of antibiotic use in patients waiting for transplant was significantly decreased compared to the transplanted patients .The mean duration of antibiotic use in patients candidate for transplantation is significantly longer than the transplanted ones before performing ASP (27.12 ± 13.10 vs. 20.18 ± 12.00 days, In our study, the number and costs of antibiotics and also the number of resistant pathogens were significantly lower following the ASP implementation and monitoring. Our results demonstrated the highest reduction in consumption with linezolid, beta-lactam/beta-lactamase inhibitors, and aminoglycosides. Reducing the usage of these antibiotics plays an important role in reducing MDR. The most common ASP program interventions in our study was the discontinuation of unnecessary antibiotics, most of which were aminoglycosides, beta-lactams beta-lactamase inhibitors, and linezolid. Many studies on ASP have also concluded that most antibiotics are unnecessary in most cases. According to the research conducted by Cusini et al., 32% of all antibiotics were given unnecessarily; it occured more in the surgical wards than in the medical wards . ImpaireIn addition to demonstrating the benefits of ASP interventions, such as increased compliance with the guidelines, reduced costs, and lower resistance rates, the results of our study should be considered with caution as it has a number of limitations. For example, only antibacterial drugs were evaluated in this study, but because antifungal and antiviral drugs are also utilized in patients undergoing SOT, ASP programs for these drugs should be considered as well. Also, a detailed and comprehensive analysis of time series cannot be done from retrospective studies, and this requires prospective studies.Furthermore, our study lasted a year, whereas more accurate data can be extracted in greater detail over a longer length of time. The pattern of consumption resistance has been studied based on the phenotype, and due to the limited cost of the project, it has not been possible to study the genotypes of antibiotic resistance.In conclusion, our study showed that, the number and costs of antibiotics and also the number of resistant pathogens were significantly lower following the ASP implementation and monitoring.Additional file 1.


Phenotyping is used in plant breeding to identify genotypes with desirable characteristics, such as drought tolerance, disease resistance, and high-yield potentials. It may also be used to evaluate the effect of environmental circumstances, such as drought, heat, and salt, on plant growth and development. Wheat spike density measure is one of the most important agronomic factors relating to wheat phenotyping. Nonetheless, due to the diversity of wheat field environments, fast and accurate identification for counting wheat spikes remains one of the challenges. This study proposes a meticulously curated and annotated dataset, named as SPIKE-segm, taken from the publicly accessible SPIKE dataset, and an optimal instance segmentation approach named as WheatSpikeNet for segmenting and counting wheat spikes from field imagery. The proposed method is based on the well-known Cascade Mask RCNN architecture with model enhancements and hyperparameter tuning to provide state-of-the-art detection and segmentation performance. A comprehensive ablation analysis incorporating many architectural components of the model was performed to determine the most efficient version. In addition, the model’s hyperparameters were fine-tuned by conducting several empirical tests. ResNet50 with Deformable Convolution Network (DCN) as the backbone architecture for feature extraction, Generic RoI Extractor (GRoIE) for RoI pooling, and Side Aware Boundary Localization (SABL) for wheat spike localization comprises the final instance segmentation model. With bbox and mask mean average precision (mAP) scores of 0.9303 and 0.9416, respectively, on the test set, the proposed model achieved superior performance on the challenging SPIKE datasets. Furthermore, in comparison with other existing state-of-the-art methods, the proposed model achieved up to a 0.41% improvement of mAP in spike detection and a significant improvement of 3.46% of mAP in the segmentation tasks that will lead us to an appropriate yield estimation from wheat plants. It is tartificial intelligence (AI) in order to greatly simplify the modern crop performance monitoring process overall. The complete processing chain of steps involved in modern high throughput plant phenotyping operations, depicted in i.e., AI) for, say, object recognition, object identification and instance segmentation. This information is then used as input into subsequent data analysis procedures where correlations with genetic and environmental factors are deduced.In agriculture, scientific efforts are directed toward quantifying how a plant’s function and features are continuously affected by its environment. While these tasks of plant and crop phenotyping are not new, manually keeping track of a plant’s physical and biological attributes such as its height, growth rate, hardiness, nutritional content, and yield, as a function of environmental conditions can be extremely time and labor demanding. Modern high throughput plant and crop phenotyping methods, on the other hand, involving image-based information, captured by configurable land-based equipment or drones with minimal effort and mostly with little expense, are able to collect significantly more data in a considerably shorter length of time. The challenge now is to effectively and efficiently process this vast amount of data, and derive as accurate information as possible about plant growth and development in an automatic way and in a practical period of time. To meet this challenge cutting-edge agricultural technology is now making use of convoluted neural network (CNN) analysis systems have been generated including, but not limited to, Faster R-CNN, Cascade R-CNN, Mask R-CNN , which employ region suggestions in conjunction with CNN characteristics , Generic RoI Extractor (GRoIE), and Side Aware Boundary Localization (SABL). Details of these modifications and an ablation analysis between these components have been explained in Section 3 and Section 4. In terms of spike segmentation, based on the results presented in Section 4, we have determined that our proposed method is more accurate, efficient, applicable, and robust than other existing methods (see Section 4). Finally, the conclusions are summarized in Section 5.To meet the requirements of our study, we have first curated and annotated a wheat spike segmentation dataset, 2All facets of modern life are becoming more dependent on technology. The agricultural industry has been profoundly altered by technological advancements. Many researchers in the field of agriculture are utilizing AI to modernize age-old practices, therefore increasing output while decreasing the workload of farm workers and their demands on the environment. Many techniques for phenotyping plants have been reported by scientists in the field. Different deep-learning approaches have been proposed for segmenting and counting wheat spikes.deep learning (DL)-based method for the detection, recognition, and counting of many wheat spikes. On average, the suggested technique was 99% precise, 95% accurate, and 97% robust when used to count spikes in images their data set. The SpikeSegNet approach achieved sufficient robustness based on a data set containing illuminated images, with no significant drop in segmentation performance. Despite extensive testing under a variety of lighting conditions, results from the lab are not feasible to apply to real-life scenarios. On the other hand, the proposed method requires sufficiently high-quality images to be able to detect and count spikes.Misra et al.  introducaverage precision (AP) equal to 0.904% and 0.907% were achieved, respectively, while a value of 99.29% was found for the precision with which wheat spikes might be enumerated. Even though both bounding box and mask segmentation have room for improvement in terms of average precision. An improved performance of Wheat-Net was found in the experiments presented in this study, although when it came to identifying the base of wheat spikes, the model encountered several segmentation issues; due to their similarity in color, texture, and form to the plant background, the connected spike and stem made up a blurry boundary.Zhang et al.  suggesteregion-based convolutional neural network (R-CNN) model for detecting and evaluating wheat spikes in ground-based images. Faster R-CNN was chosen as the network model to be instructed in this article’s training set. Images were fed into a pre-trained VGG-16 prototype, which was subsequently utilized to automatically extract features, and then sent to the region proposal network (RPN) to generate bounding boxes (Bbox-es), and finally to the classification network to be labelled as spikes or background. In-field images were captured using high-definition RGB cameras to create a spike data set called SPIKE, which was used to train a Faster RCNN architecture. Both the average accuracy and F1 score for the model were 93.4% and 0.95, respectively. Nonetheless, no actual spike segmentation work is carried out here. However, the model’s inaccuracy increased when dealing with partially covered spikes, especially in high-density locations.The work by Hasan et al.  presentebi-directional feature pyramid network) for more efficient implementation of multi-scale data, and the use of Soft-NMS (non-maximum suppression) to address the occlusion problem − make it an upgraded edition of the RetinaNet framework. The authors trained and evaluated their technique using the GWHD data set, which was supplemented with pictures from the wheat-wheat grass spike detection (WSD) dataset. One drawback of this approach is that it may generate many bounding boxes for the same spike. Again, the outcome differs depending on the growth stage of the crops. Despite Mean average accuracy (mAP) rates for wheat spike identification achieved by the model were 92.62%, with a count detection capacity of 92.88%.A strategy based on SpikeRetinaNet was developed by Wen et al.  to recogregional proposal network (RPN). A faster R-CNN-based technique was found to be effective at recognizing stem anchors in 2D views from 3D Lidar pictures. The model has a drawback in that if the stem is totally missing in the scanned data, the system will not be able to recognize the individual maize plant, which will not be able to develop into an individual stem. The suggested approach was only tested on premature maize plants, and its efficacy for segmenting adult maize plants requires more study.For segmenting individual corn against a ground background, Jin et al.  suggesteZhou et al.  proposedfusarium head blight (FHB) causes significant losses in quantity and quality of wheat grains. To build the feature pyramid and to extract features, Mask-RCNN relied on a network similar to ResNet-101 called the feature pyramid network (FPN). After full-sized image of wheat spikes were used to create mask images, Mask-RCNN was used to forecast unhealthy spots on each spike. Despite the bigger dataset, naturally infected wheat spikes were left out, and the dataset was collected under controlled environmental settings. The detection rates for wheat spikes using this procedure were 77.76%, and for infected regions, they were 98.81%. By comparing the predicted wheat FHB severity value to the actual value, they were able to attain an accuracy of 77.19% in their predictions.Su et al.  suggesteMost spike segmentation approaches have only been evaluated on random data sets in the lab, making it hard to determine whether or not they will provide correct findings when applied to real-world images. The images were taken in controlled settings, resulting in good resolution and recognizable spikes; the number of obscured spikes is very minimal. As a result, the techniques often perform poorly under real-world imaging conditions when only partially visible spikes are present, particularly when the resolution is low. In contrast, we evaluated our model (described in the next section) on low-resolution, real-world image data and found an increased capacity to segment (and count) partially hidden spikes.3The objective of our efforts is the development of a faster, more robust and more accurate method of identifying and counting wheat spikes in land-based field images. A flow-chart diagram describing the process involved in our system is shown in 3.1Triticum aestivum L.): Drysdale, Excalibur, Gladius, Gregory, Kukri, Mace, Magenta, RAC875, Scout, and Yitpi, were cultivated in the areas. Having spikes of varying shapes and sizes increases the versatility of the data set for the task of recognizing different wheat varieties (not of a focus of this work). Three fertilizer treatments were applied to each variety of wheat: no fertilizer treatment, early treatment, and late treatment, in order to establish the effect of fertilizer on wheat spike development. Each variety and each different treatment was replicated three times for a statistical analysis. Two-thirds of the plots were given the industry-standard fertilizer dose of 80 kilograms of nitrogen, 40 kilograms of phosphorus, and 40 kilograms of potassium per hectare, whereas the remaining thirty plots were left untreated.For this research, we have utilized the SPIKE data set that was established by Hasan et al. . The SPIThree cameras were mounted on an overhead rail in the center of a steel-framed, four-wheeled cart shown in Focal length —18 mm,Aperture — f/9.0,ISO — automaticExposure time — 1/500 s3.2For the segmentation of wheat spikes from field images, we manually annotated images from the SPIKE data set to create training, testing and validation sets. The images in this data set are divided into the following three color classes: GSYC - green spike, yellow canopy; GSGC - green spike, green canopy; YSYC - yellow spike, yellow canopy. We maintained this same distribution of images, which correspond to the different growth stages of wheat. Table 1 To annotate the images in the data set we used the Roboflow Web API . Wheat s3.33.3.1instance segmentation method to be the most suitable for the task at hand. The high resolution of the plot images, allowed the segmentation model to detect, localize and segment spikes from features at the instance level and thus be able to count the detected spikes as well. We use the Cascade Mask R-CNN model, a two-stage object detection and segmentation approach described by Cai et al.   to model geometric transformations of objects in images. The rk (DCN)  addresserk (DCN) Figure 8region of interest (ROI) features using a feature pyramid network (FPN)   (generic RoI extractor (GRoIE)   and balaid (BFP)  sub-netwrk (RPN) , which g (GRoIE) , which u (GRoIE) .bounding box (bbox) head of our model  approach, proposed by Wang et al. . Early model instability can result from a large LR. In our model we used a linear warm-up LR approach (average precision (AP) for an intersection over union (IoU) measure of 0.5, MultiStepLR converges significantly sooner .Deep learning networks need to adaptably fine-tune the approach  for traik then LR must also be multiplied by k, while other hyper-parameters remain unchanged. The base batch size and LR for the auto scaling rule was set to 16 and 0.01, respectively. So, with a batch size of 2 and 1, the LR was reduced by a factor of 8 and 16, respectively. This is done so that the models’ are not affected negatively by having different batch sizes due to memory constraints. We observed the impact of utilizing the auto-scaling rule in the ablation study part of our experiments. Furthermore, we also employed auto-scaling LR based on the batch size and number of images per gpu to obtain the LR for our model . Accordix800 resolution, random flipping with a 0.5 probability of being vertical or horizontal, and finally padding the images with up-sampling to a multiple of 32. These data augmentations help enhance the robustness of the model by providing it with more varied training data. The backbone network of the model was initialized with weights from a pre-trained ResNet50 model and parameters of other modules was initialized randomly with different initialization methods such as Kaiming initialization , side aware boundary localization (SABL) for bounding box regression, and auto-scaling LR rule. During training, several data augmentation treatments were applied, namely, resizing the images to 1333lization  and Xavilization .As the model has a cascaded structure with separate bbox and mask heads for detection and segmentation tasks, the overall loss function of the model takes the following form,a are set to  for all three stages consecutively.Here, Smooth L1 loss is used for both region proposals and bounding box regression loss, which can be defined as follows:pred represents the predicted bbox (or region), target represents the target bbox (or region), and β is the threshold parameter, which was set to 1.0/9.0 for the RPN head and 0.1 for all the three bbox heads.Here, For classifying the bounding box region as well as the pixel in the segmentation mask, Cross Entropy (CE) loss function is used, defined as following:iy is the target class and ip is the predicted class of the bounding box (or pixel), and N = 2 as we are only classifying between the spike and background classes.Here, 3.3.3average precision (AP) metric because of its representativeness and simplicity. AP measures the area under the Precision-Recall (P-R) curve, where precision and recall are defined by Equations (4) and (5). Thus, AP can be defined by Equation (6). According to the COCO evaluation protocol, AP can be measured at different thresholds for intersection over union (IoU) measures, such as IoU = 0.5 (PASCAL VOC metric), IoU = 0.75 (strict metric), and IoU = 0.5: 0.95: 0.05   .S| denotes the numerical size of the set S.where |true positive (TP) - refers to the case where the model correctly detects a region as a spike, false positive (FP) — refers to the case where the model incorrectly detects a background region as a spike, or detects the same spike as multiple ones; and false negative (FN) — is where the model incorrectly classifies an actual spike as background. P (ir) is the measured precision at recall i.r The precision at each recall level, ir, is interpolated by taking the maximum precision-measured for which the corresponding recall exceeds. A and B represent the predicted and target bbox, respectively, in Equation 7.In the above and below, 3.3.4Training and testing of all the experimental models were done using a Ryzen-5 2600x (6-core) processor, 16GB system RAM and NVIDIA RTX 2070 GPU with 8GB VRAM (unless specified otherwise). We also utilized the open-source object detection and instance segmentation framework MMDetection  based on44.1We report the average precision (AP) of our model on the test set to evaluate its performance. The test set contains 15 images from three different growth stages of wheat and contains a total of 1243 wheat spikes. In our experiment, the object detection score refers to the AP value at a specified threshold, calculated using Equation 6, for the final bounding box prediction stage of our model’s network. Segmentation score refers to the same metric, but for the final segmentation mask output stage. Our model achieved an object detection score of 0.93 and segmentation score of 0.9404 for AP at IoU = 0.5, 0.801 and 0.8018 for AP at IoU= 0.75, and 0.678 and 0.6459 for AP at IoU = [0.5: 0.95: 0.05]. In the case of a dense environment such as a wheat plot, detection models tend to predict many false positives. In our case, the high AP values indicate a relatively low detection rate of false positives.The detection performance of the model can also be evaluated through the count results on the test set. We set an IoU threshold of 0.5 for a spike prediction to be considered true positive and counted the TP, FP and FN results for each image. In addition to these summary measures, we visualize in Some example cases of spike occlusion, such as when occluded by another spike or awns or leaves, are shown in 4.2We conducted an ablation analysis in which we omitted one or more of different components of the complete model architecture. The objective of this study was to assess the significance and quantitative impact of the model’s individual components. Such a study can assist in identifying the most important features of the model structure and provide some direction for future enhancements. During the study, we created different versions of the model with different combinations of the components. We refer to these versions asDeformable Convolution Network (DCN) ,Generic RoI Extractor (GRoIE) ,Side Aware Boundary Localization (SABL) , andAuto-scaling LR .To evaluate the individual as well as combined impact of these components on the model’s performance, we set a baseline architecture based on a ResNet50 backbone, image scale = 1333 × 800, batch size = 2, and LR = 0.00125 (unless specified otherwise).Not surprisingly, a comparison of these alternative approaches, shown in Meanwhile, without GRoIE, the SABLHead can regress the bounding box from the RoI that is extracted from the top-most level of FPN thus getting bounding boxes with better IoU scores. This is reflected in the increased score of both AP (IoU = 0.75) and mAP metrics. Furthermore, with the help of GRoIE, SABLHead is able to regress bounding boxes from a larger number of samples or RoIs extracted. This led to more detected bounding boxes with an IoU score of at least 0.5, resulting in reduced false negatives and increased true positives, which is reflected in the higher value for AP (IoU = 0.5).4.3In order to get an overview of our model’s performance in comparison with other existing models, we conducted several experiments involving models with different backbones, LR schedulers and architectures. Our benchmarked scores are reported in ResNet50 with deformable convolution backbone achieved on average 1.42% and 1.99% better scores than the default ResNet50 backbone across all evaluation metrics for bbox and mask segmentation respectively. We also compared different learning rate schedulers to see which one would be better suited for our case. We reported the benchmark scores between Multistep and Linear Annealing learning rate schedulers. While Linear annealing showed a 0.3% and 0.12% increase in AP (IoU = 0.5), it was slower in terms of model convergence. So, we opted for using the Multistep LR scheduler throughout the rest of our experiments as it let the model converge to an optimal solution in less epochs than linear annealing LR scheduler. Model with LR scheduler also has around 23% slower inference speed compared to Multi-step LR scheduler, so there is a bit of tradeoff between precision and speed when choosing between different LR schedulers. We have made the decision to use the Linear Annealing Learning Rate (LR) scheduler in our model because it could produce better mAP scores (at IoU=0.5), but with a little trade-off in inference speed.Finally, we compared different object detection and instance segmentation architectures, namely, Faster RCNN, Mask RCNN, and ours as in Cascade Mask RCNN, with a similar configuration of hyperparameters, to figure out the best architecture for our model. Our model with the Cascade Mask RCNN architecture outperforms all other methods by an average of 2.79% for bbox and 3.52% for the mask at AP (IoU = 0.5) while converging at only 70 epochs. Overall, we showed that our architecture and optimization strategy of choice provides a significant performance boost over all other compared architectures and strategies.4.4In comparison to other existing methods Hasan et al. , Wen et Our approach has shown some promising results in spike segmentation. However, there are a few limitations. When a leaf’s color is close to that of a spike, the model misidentifies the leaf as a spike, as seen in 5This research addresses the phenotyping challenge of accurately segmenting wheat spikes from field images with high precision and persistence. Such images must be able to non-destructively discern, count, and segment wheat spikes in order to accurately estimate wheat production. This research concentrates on enhancing deep learning-based methods to handle the complexities of field images, such as illumination, occlusions, and various growth stages. The proposed method introduces a novel architecture for the spike segmentation task which differs significantly from existing methods since for accurate detection and segmentation of the spike regions we fused the Cascade Mask RCNN with other extra precising techniques of DCN, GRoIE, SABL and Auto-scaling LR. Several iterations of repeated trials were applied to fine-tune and optimize the architecture and hyper-parameters of the model to satisfy the criteria. Along with a few limitations such as human annotation error, misidentifies the leaf as a spike, overlooking due to a high density and limited visibility of spikes; the approach offers a significant improvement over existing techniques, which have hitherto been recognized as state-of-the-art, such as Zhang et al., Su et ahttps://figshare.com/projects/WheatSpikeNet_An_Improved_Wheat_Spike_Segmentation_Model_for_Accurate_Counting_from_Field_Imaging/163225.The codebase developed and the datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: MMH and SJM contributed to conception and design of the study. MMH, MAB, and MI organized the database. MAB, and MI performed the statistical analysis. MAB and MI wrote the first draft of the manuscript. AKMA, MMH, and MAH contributed to the visualization and representations in the draft. MMH, SJM, MAB, and MI wrote sections of the manuscript. MMH, SAA, AKMA, and SJM proofread the manuscript for final submission. MMH, and SJM supervised the project. MAH, and SAA contributed in project administration. All authors contributed to manuscript revision, read, and approved the submitted version.