May 10, 2021
An artificial intelligence (AI)-enabled ECG algorithm integrated into routine care can increase the diagnosis of low ejection fraction (EF), according to new randomized trial data.
“Because ECG is a low-cost test frequently performed for a variety of purposes, the algorithm could potentially improve early diagnosis and treatment in broad populations,” write Xiaoxi Yao, PhD (Mayo Clinic, Rochester, MN), and colleagues in their study published online last week in
Nature Medicine. The algorithm uses neural networks to predict a high likelihood of low EF, an often-missed predictor of adverse events, based on standard 12-lead electrocardiogram data.
But the success of the technology—developed by the same team who recently published results on identifying long QT syndrome in a similar fashion—is dependent on it actually being used by clinicians, Yao told TCTMD. Since a myriad of AI-based algorithms are created daily, and money and IT resources to be engaged, “we cannot afford to implement all the AI algorithms into the EHR [electronic health record],” she said, noting the importance of randomized trials in this space.