New AI Tool Finds Rare Variants Linked to Heart Disease in 17 Genes

Using an advanced artificial intelligence tool, researchers at the Icahn School of Medicine at Mount Sinai have identified rare coding variants in 17 genes that shed light on the molecular basis of coronary artery disease (CAD), the leading cause of morbidity and mortality worldwide. The discoveries, detailed in the June 11 online issue of Nature Genetics, reveal genetic factors impacting heart disease that open new avenues for targeted treatments and personalized approaches to cardiovascular care. The investigators used an in silico, or computer-derived, score for coronary artery disease (ISCAD) that holistically represents CAD, as described in a previous paper by the team in The Lancet. The ISCAD score incorporates hundreds of different clinical features from the electronic health record, including vital signs, laboratory test results, medications, symptoms, and diagnoses. To build the score, they trained machine learning models on the electronic health records of 604,914 individuals

Researchers Develop AI Model to Better Predict Which Drugs May Cause Birth Defects

Data scientists at the Icahn School of Medicine at Mount Sinai in New York and colleagues have created an artificial intelligence model that may more accurately predict which existing medicines, not currently classified as harmful, may in fact lead to congenital disabilities. The model, or “knowledge graph,” described in the July 17 issue of the Nature journal Communications Medicine, also has the potential to predict the involvement of pre-clinical compounds that may harm the developing fetus. The study is the first known of its kind to use knowledge graphs to integrate various data types to investigate the causes of congenital disabilities.

AI-guided screening uses ECG data to detect a hidden risk factor for stroke

An AI-guided targeted screening strategy is effective in detecting new cases of atrial fibrillation that would not have come to attention in routine clinical care.
This strategy could reduce the number of undiagnosed cases of atrial fibrillation, and prevent stroke and death in millions of patients across the globe.