Researchers led by Brandon Fornwalt at the Pennsylvania-based Geisinger Health System put their machine learning model to work studying the results of some 1.8 million electrocardiogram (ECG) heart scans, hoping the neural network would derive patterns from the heaps of data. Predicting the risk of a heart attack or other heart-related issues, the AI performed better than its human counterparts, consistently scoring above flesh-and-blood doctors. Even for ECG results that cardiologists determined to be normal, the AI was able to pick up on other patterns and accurately predict fatal health risks within a year's time. "That finding suggests that the model is seeing things that humans probably can't see, or at least that we just ignore and think are normal," Fornwalt said. AI can potentially teach us things that we've been maybe misinterpreting for decades.

Scientists at the University of Oxford used AI to develop a new biomarker, or'fingerprint' called fat radiomic profile (FRP). The technology could identify people at high risk of a fatal heart attack at least five years before it strikes by detecting biological red flags in the perivascular space lining blood vessels which supply blood to the heart, Tech Explorist reports. Currently, there are no methods routinely by specialists that can spot the majority of the fundamental warnings for a future heart attack. For this study, scientists primarily used fat biopsies from 167 people undergoing cardiac surgery. They then analyzed the expression of genes related with inflammation, scarring, and new blood vessel formation, and matched these to the CCTA scan images to figure out which highlights best demonstrate changes to the fat encompassing the heart vessels, called perivascular fat.