Machine Learning finds previously unknown ECG signal for cardiac death risk
scientificamerican.com
scientificamerican.com
* https://www.nature.com/articles/d41586-026-01806-z
> Paired with the original network and guided by its risk score, the generative model reworked a real low-risk patient’s ECG step by step, morphing it smoothly into a high-risk version of the same trace. Many of the features the model keyed on were already familiar to cardiologists.
> One feature, though, had never been described in the medical literature: a subtle slurring in one ECG lead called aVL, suggesting that the heart’s electrical signal was fragmenting as it moved through muscle.
> Changxin Lai, a biomedical engineer at Johns Hopkins University who wrote an accompanying analysis in Nature and was not involved in the study, says this is why the work stands out. “The ECG has been around for more than 100 years, and this kind of data has been carefully evaluated by generations of cardiologists,” he says. “We extracted new knowledge from an artificial intelligence model.”