Machine learning models enhance detection of arrhythmogenic right ventricular cardiomyopathy

Machine Learning Health 2026 Open access

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Authors: Kwaku K. Quansah, Sean A. Murphy, Esther Kwon, Emma C. Anderson, Crystal Tichnell, Brittney A. Murray, Sean Paul Gaine, Alessio Gasperetti, Cynthia A. James, Hugh Calkins, Richard T. Carrick, Chulan Kwon
DOI: 10.1088/3049-477x/ae451a
Cited by: 1
References: 33

Abstract

< 0.05), while differences vs the remaining five models were not statistically significant. The next-best model (Random Forest) showed a minimal performance gap (ΔAUC = 0.008), whereas low-ranked models showed larger deficits (ΔAUC = 0.040-0.042). An ECG-only version of the GBT model achieved an AUC of 0.884, exceeding the previously reported ECG-deep learning waveform model (AUC = 0.87). GBT performs best among evaluated algorithms and offers clinically interpretable feature relevance consistent with task force criteria for ARVC diagnosis. An ECG-only deployment supports early triage, while the multimodal model functions as confirmatory decision support after advanced testing. These findings support a tiered ML-assisted diagnostic strategy for ARVC and justify prospective external validation in broader clinical settings.

Fields of Study & Subjects

Cardiovascular Effects of Exercise Cardiac electrophysiology and arrhythmias Atrial Fibrillation Management and Outcomes

Publication details

Journal / source
Machine Learning Health
Publisher
IOP Publishing
Volume
2
Issue
1
Pages
010501–010501
Publication date
12 February 2026
Language
EN