Comprehensive Summary
Inan et al. conducted a retrospective cross-sectional study evaluating deep-learning models for classifying wide-complex tachyarrhythmias (WCT) as ventricular tachycardia (VT) or supraventricular tachycardia (SVT) with aberrant conduction. The dataset included 652 WCT and 248 normal sinus rhythm ECGs from adults treated at Ankara Bilkent City Hospital (November 2021–March 2023). Pretrained Residual Network (ResNet-18, ResNet-34, ResNet-50) architectures were fine-tuned via transfer learning for three-class classification. All models achieved approximately 95% diagnostic accuracy with strong specificity and generalization, outperforming traditional algorithms such as the Brugada and Verecki criteria. Among the architectures, ResNet-50 showed the greatest sensitivity for VT detection, whereas ResNet-34 achieved the highest specificity.
Outcomes and Implications
The authors note that accurately distinguishing VT from SVT in WCT is critical in emergencies, as misclassification can delay appropriate treatment and elevate clinical risk. Their findings suggest that deep-learning models, particularly ResNet-based architectures, can serve as effective decision-support tools to enhance ECG interpretation accuracy in emergency departments. Inan et al. emphasize that their models hold promise for improving diagnostic precision and assisting clinicians in high-stakes emergency scenarios. However, the authors acknowledge limitations, including single-center data, class imbalance, and the need for external validation. They conclude that further research is needed to confirm model generalizability, interpretability, and integration into clinical workflows before real-world deployment.