A study published in the journal eBioMedicine assesses the effectiveness of electrocardiograms (ECGs) analyzed via deep learning as a tool for early COPD detection.

A poor night’s sleep portends a bleary-eyed next day, but it could also hint at diseases that will strike years down the road. A new artificial intelligence model developed by Stanford Medicine researchers and their colleagues can use physiological recordings from one night’s sleep to predict a person’s risk of developing more than 100 health conditions.

Professor Jung Kyoon Choi’s research team from the Department of Bio and Brain Engineering, in collaboration with Neogen Logic Co., Ltd., developed the new AI model to predict neoantigens.

CardioKG provides a detailed view of the heart’s structure and function which dramatically improves the accuracy of predicting which genes are linked to disease and whether existing drugs could treat them.

As rates of obesity, high blood pressure, type 2 diabetes and sleep apnea increase, cases of advanced chronic liver disease and resulting liver scarring or cirrhosis also are rising. Patients are often diagnosed based on symptoms, such as gastrointestinal bleeding, fluid retention or jaundice, which happen when liver disease has progressed to a late stage.

A new artificial intelligence model could help design antibodies that better protect the body against viruses and disease. The AI model, known as ImmunoMatch, can predict and identify correct protein pairings within antibodies, potentially helping to strengthen the immune system. The research was conducted by a team from the University of Surrey and University College London and is published in Nature Methods.