Collaborating Authors


Machine-learning test may improve kidney failure prediction in patients with diabetes


For patients with type 2 diabetes or the APOL1-HR genotype, a machine learning test integrating biomarkers and electronic health record data demonstrated improved prediction of kidney failure compared with commonly used clinical models. According to Kinsuk Chauhan, MD, MPH, of Icahn School of Medicine at Mount Sinai, and colleagues, diabetic kidney disease from type 2 diabetes accounts for 44% of all patients with end-stage kidney disease, with the APOL1 high-risk genotypes also associated with increased risk for chronic kidney disease progression and eGFR decline that may ultimately result in kidney failure. "Even though these populations are on average higher risk than the general population, accurate prediction of who will have rapid kidney function decline (RKFD) and worse kidney outcomes is lacking," the researchers wrote, noting that the current standard of using the kidney failure risk equation to predict ESKD has only been validated in patients who already have kidney disease and not in those with preserved kidney function at baseline. "Widespread electronic health records (EHR) usage provides the potential to leverage thousands of clinical features," the researchers added. "Standard statistical approaches are inadequate to leverage this data due to feature volume, unaligned nature of data and correlation structure."

Coronavirus doctor's diary: How gardening could help in the fight against obesity

BBC News

Being overweight puts you at greater risk of serious illness or death from Covid-19, experts say - and now new anti-obesity strategies have been launched around the UK. In Bradford, community schemes to promote healthy lifestyles offers a novel approach to the problem. Dr John Wright of the city's Royal Infirmary explains why radical thinking is necessary. Our complete concentration on Covid-19 has concealed another global pandemic that has been more insidious but much more harmful: obesity. Early in the pandemic, we spotted common patterns in our sickest Covid-19 patients - they were more likely to have diabetes and heart disease and, in particular, to be obese.

Artificial intelligence against Diabetes and COVID-19 - GreatLearning


Artificial Intelligence has multiple applications in the healthcare domain. We have also seen many AI solutions being developed to identify symptoms of COVID-19 among patients. Here's one such case of AI-based screening of visitors at Mumbai railway stations to identify COVID-19 symptoms. Also, the application of Artificial Intelligence in adjusting insulin dose to control glucose levels among Type I Diabetes patients. Body-screening facility "FebriEye thermal cameras" have been set up at Chhatrapati Shivaji Maharaj Terminus and Lokmanya Tilak Terminus in Mumbai to scan passengers for COVID-19 symptoms.

Data Science:Hands-on Diabetes Prediction with Pyspark MLlib


This is a Hands-on 1- hour Machine Learning Project using Pyspark. Pyspark is the collaboration of Apache Spark and Python. PySpark is a tool used in Big Data Analytics. Apache Spark is an open-source cluster-computing framework, built around speed, ease of use, and streaming analytics whereas Python is a general-purpose, high-level programming language. It provides a wide range of libraries and is majorly used for Machine Learning and Real-Time Streaming Analytics.

End to End Machine Learning


Hospital readmission rates for certain conditions such as diabetes are now considered an indicator of hospital quality and also have a negative impact on the cost of care. We used the medical dataset available on the UCI website to find the best models that can help predict the readmission of diabetic patients. The stakeholder in this project will be hospital officials who can use the results to determine which patients have the best chances of readmission. This will save millions of money in the hospital and also improve the quality of health care. The first task you are asked to perform is to build a model of Diabetes Readmission Prediction.

Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients


Background/aims Human grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard. Methods Retinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard. Results Sensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy. Conclusion The algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed.

AI is going to revolutionize the way we work – Kunal Kinalekar – The Tech Pod


BeatO is a smart diabetes management system powered by AI to provide actionable insights to manage diabetes and offers an entire ecosystem to help users manage their illness. BeatO combines cost-effective hardware (the BeatO smartphone connected glucometer) with the BeatO app to support end-to-end management for their users. In an interaction with The Tech Pod, Kunal speaks about the future of AI in India. Tell us something about yourself and what does your company do? Well, I work as the Chief Technology Officer at BeatO, a Health Arx Technologies Pvt Ltd., with expertise in delivering value-based business transformations and engineering feasibility. I am responsible for developing innovative solutions, using AI and IoT along with software implementation at BeatO.

AI Detects Serious Eye Disease In Diabetic Patients - Pioneering Minds


Results from the largest study of artificial intelligence use in the English Diabetic Eye Screening Programme (DESP), have shown that the technology can accurately detect serious eye disease among those with diabetes (retinopathy) and could halve the human workload associated with screening for diabetic eye disease, saving millions of pounds annually. These findings could also pave the way for the technology to be used to reduce the backlog in eye screening appointments following the COVID-19 lockdown. The study uses the images from 30,000 patient scans (120,000 images) in the DESP to look for signs of damage using the EyeArt artificial intelligence eye screening technology. The results showed that the technology has 95.7% accuracy for detecting damage that would require referral to specialist services, but 100% accuracy for moderate to severe retinopathy or serious disease that could lead to vision loss.

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning


The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the number of Ki-67-positive and Ki-67-negative tumor cells within a subjectively picked hot-spot. Intraobserver variability in this procedure as well as difficulty in distinguishing tumor from non-tumor cells can lead to inaccurate Ki-67 indices and possibly incorrect tumor grades. We introduce two computational tools that utilize Ki-67 and synaptophysin double-immunostained (DS) slides to improve the accuracy of Ki-67 index quantitation in GI-NETs: (1) Synaptophysin-KI-Estimator (SKIE), a pipeline automating Ki-67 index quantitation via whole-slide image (WSI) analysis and (2) deep-SKIE, a deep learner-based approach where a Ki-67 index heatmap is generated throughout the tumor. Ki-67 indices for 50 GI-NETs were quantitated using SKIE and compared with DS slide assessments by three pathologists using a microscope and a fourth pathologist via manually ticking off each cell, the latter of which was deemed the gold standard (GS). Compared to the GS, SKIE achieved a grading accuracy of 90% and substantial agreement (linear-weighted Cohen’s kappa 0.62). Using DS WSIs, deep-SKIE displayed a training, validation, and testing accuracy of 98.4%, 90.9%, and 91.0%, respectively, significantly higher than using SS WSIs. Since DS slides are not standard clinical practice, we also integrated a cycle generative adversarial network into our pipeline to transform SS into DS WSIs. The proposed methods can improve accuracy and potentially save a significant amount of time if implemented into clinical practice.