More people were correctly identified as having long QT syndrome (LQTS) when their electrocardiograms were analyzed with artificial intelligence (AI) rather than the QTc metric alone, researchers reported.
The task of differentiating people with LQTS from peers, evaluated for the condition but not diagnosed, was performed better by a convolutional neural network — with an area under the receiver operating characteristic curve (AUC) of 0.900 — than by ECG-derived QTc classification (AUC 0.824), according to Michael Ackerman, MD, PhD, of Mayo Clinic in Rochester, Minnesota, and colleagues.
