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Machine learning models enhance detection of arrhythmogenic right ventricular cardiomyopathy

Year: 2026

Type: article

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.

Source: Machine Learning Health

DOI: 10.1088/3049-477x/ae451a