Cedars-Sinai research shows deep learning model could improve AFib detection

Investigators at Cedars-Sinai's Smidt Heart Institute in Los Angeles developed an artificial intelligence (AI) method that effectively detects atrial fibrillation, a condition characterized by abnormal heart rhythms, from echocardiogram imaging. The deep learning model was trained with over 100,000 echocardiogram videos to distinguish between normal heart rhythms and those indicating atrial fibrillation. Remarkably, this AI can identify patients likely to develop or who have had atrial fibrillation within 90 days, surpassing traditional risk estimation methods. This advancement highlights the potential for AI to play a significant role in early cardiac care, especially for conditions like atrial fibrillation that are challenging to diagnose due to their intermittent nature. The technology promises to improve early detection and treatment, potentially preventing serious cardiovascular events in patients with this often undetected arrhythmia.

Opens on Healthcare IT News