An AI-based Holter analysis platform was noninferior to conventional analysis for detecting major rhythm abnormalities and was significantly faster (4.4 vs 6.0 minutes; P<0.001).
Cross-Sectional (n=1,000)
Yes
Does an artificial intelligence-based Holter analysis platform using deep neural networks improve analysis speed without compromising diagnostic accuracy compared to conventional analysis?
An AI-based Holter analysis platform using deep neural networks is faster and noninferior to conventional analysis by electrophysiologists for detecting major rhythm abnormalities.
Background Holter analysis requires significant clinical resources to achieve a high‐quality diagnosis. This study sought to assess whether an artificial intelligence (AI)‐based Holter analysis platform using deep neural networks is noninferior to a conventional one used in clinical routine in detecting a major rhythm abnormality. Methods and Results A total of 1000 Holter (24‐hour) recordings were collected from 3 tertiary hospitals. Recordings were independently analyzed by cardiologists for the AI‐based platform and by electrophysiologists as part of clinical practice for the conventional platform. For each Holter, diagnostic performance was evaluated and compared through the analysis of the presence or absence of 5 predefined cardiac abnormalities: pauses, ventricular tachycardia, atrial fibrillation/flutter/tachycardia, high‐grade atrioventricular block, and high burden of premature ventricular complex (>10%). Analysis duration was monitored. The deep neural network–based platform was noninferior to the conventional one in its ability to detect a major rhythm abnormality. There were no statistically significant differences between AI‐based and classical platforms regarding the sensitivity and specificity to detect the predefined abnormalities except for atrial fibrillation and ventricular tachycardia (atrial fibrillation, 0.98 versus 0.91 and 0.98 versus 1.00; pause, 0.95 versus 1.00 and 1.00 versus 1. 00; premature ventricular contractions, 0.96 versus 0.87 and 1.00 versus 1.00; ventricular tachycardia, 0.97 versus 0.68 and 0.99 versus 1.00; atrioventricular block, 0.93 versus 0.57 and 0.99 versus 1.00). The AI‐based analysis was >25% faster than the conventional one (4.4 versus 6.0 minutes; P <0.001). Conclusions These preliminary findings suggest that an AI‐based strategy for the analysis of Holter recordings is faster and at least as accurate as a conventional analysis by electrophysiologists.
Fiorina et al. (2022) conducted a cross-sectional in Cardiac rhythm abnormalities (n=1,000). AI-based Holter analysis platform using deep neural networks vs. Conventional Holter analysis by electrophysiologists was evaluated on Detection of a major rhythm abnormality. An AI-based Holter analysis platform was noninferior to conventional analysis for detecting major rhythm abnormalities and was significantly faster (4.4 vs 6.0 minutes; P<0.001).