The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline

Diabetic Retinopathy is one of the common eye diseases due to the complication of diabetes mellitus. Cotton wool spots, rough exudates, haemorrhages and microaneurysms are the symptoms of the diabetic retinopathy due to the fluid leakage that is caused by the high blood glucose level disorder. Early treatment to prevent a permanent blindness is important as it could save the diabetic retinopathy vision. Hence, in this study, we proposed to employ an automated detection method to diagnose the diabetic retinopathy. The dataset was obtained from the Kaggle Database and been divided for training, testing and validation purposes. Furthermore, Transfer Learning models, namely VGG19 were employed to extract the features before being processed by Machine Learning classifiers which are SVM, kNN and RF to classify the diabetic retinopathy. VGG19-SVM pipeline produced the best accuracy in training, testing and validation processes, achieving 99, 99 and 96 percents respectively

The diagnosis of diabetic retinopathy : An evaluation of different classifiers with the inception V3 model as a feature extractor

Diabetic Retinopathy (DR) is a type of eye disease that is caused by diabetes mellitus. The elevated blood glucose level causes the expansion of the blood vessels that results in the leaking of the blood and other fluids. DR is a silent disease in which those inflicted with it are unaware until irregularities in the retina have advanced to the point where treatment is difficult or impossible to administer, resulting in them losing their sight completely. However, it is worth noting that early treatment can solve this problem. Hence, the purpose of this study is to develop a transfer learning pipeline for diagnosing DR. The data in the present study was obtained from the Kaggle database, and the pre-trained InceptionV3 model was employed to extract the features from the images acquired. The features are fed into the three different classifiers, namely, Support Vector Machine (SVM), k-Nearest Neighbour (kNN) and the Random Forest (RF). It was shown from the present investigation that the InceptionV3-SVM pipeline demonstrated the best performance by achieving 100%, 98% and 96% classification accuracy for the training, testing and validation dataset. The results further suggest the possible deployment of the pipeline for the diagnosis of DR

Community Control of Hypertension and Diabetes (CoCo-HD) program in the Indian states of Kerala and Tamil Nadu : a study protocol for a type 3 hybrid trial

Introduction: India grapples with a formidable health challenge, with an estimated 315 million adults afflicted with hypertension and 100 million living with diabetes mellitus. Alarming statistics reveal rates for poor treatment and control of hypertension and diabetes. In response to these pressing needs, the Community Control of Hypertension and Diabetes (CoCo-HD) program aims to implement structured lifestyle interventions at scale in the southern Indian states of Kerala and Tamil Nadu. Aims: This research is designed to evaluate the implementation outcomes of peer support programs and community mobilisation strategies in overcoming barriers and maximising enablers for effective diabetes and hypertension prevention and control. Furthermore, it will identify contextual factors that influence intervention scalability and it will also evaluate the program’s value and return on investment through economic evaluation. Methods: The CoCo-HD program is underpinned by a longstanding collaborative effort, engaging stakeholders to co-design comprehensive solutions that will be scalable in the two states. This entails equipping community health workers with tailored training and fostering community engagement, with a primary focus on leveraging peer supportat scale in these communities. The evaluation will undertake a hybrid type III trial in, Kerala and Tamil Nadu states, guided by the Institute for Health Improvement framework. The evaluation framework is underpinned by the application of three frameworks, RE-AIM, Normalisation Process Theory, and the Consolidated Framework for Implementation Research. Evaluation metrics include clinical outcomes: diabetes and hypertension control rates, as well as behavioural, physical, and biochemical measurements and treatment adherence. Discussion: The anticipated outcomes of this study hold immense promise, offering important learnings into effective scaling up of lifestyle interventions for hypertension and diabetes control in low- and middle-income countries (LMICs). By identifying effective implementation strategies and contextual determinants, this research has the potential to lead to important changes in healthcare delivery systems. Conclusions: The project will provide valuable evidence for the scaling-up of structured lifestyle interventions within the healthcare systems of Kerala and Tamil Nadu, thus facilitating their future adaptation to diverse settings in India and other LMICs.Peer reviewe

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83-0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders