Combining radiomics and machine learning for enhanced localization of premature ventricular contractions
By
Jingjie Liu
Shiyu Dai
Lingxuan Hou
Boyang Zang
Yang Liu
Chongfu Jia
Xiaomeng Yin
May 15, 2026
Clinical Scorecard: Integrating Radiomics with Machine Learning to Improve Detection of Premature Ventricular Contractions
At a Glance
Category Detail
Condition Premature Ventricular Contractions (PVC) Key Mechanisms Abnormal excitability of ventricular myocardial cells leading to premature depolarization. Target Population Patients diagnosed with PVC undergoing first-time radiofrequency catheter ablation. Care Setting Single-center, retrospective diagnostic accuracy study.
Key Highlights
PVC can lead to symptoms like palpitations and chest tightness. Catheter ablation is highly effective for managing PVC. CCTA-based radiomics can identify microstructural changes associated with PVC. Machine learning models have shown improved localization accuracy for PVC origins. The study included 304 patients with documented PVC origins. Guideline-Based Recommendations
Diagnosis
PVC diagnosed by ECG and Holter monitoring. CCTA used for localization of PVC origin. Management
Radiofrequency catheter ablation (RFCA) is recommended for treatment. Monitoring & Follow-up
Documented PVC origin confirmed by intracardiac electrophysiological mapping. Risks
Persistent PVC increases the risk of lethal arrhythmias. Patient & Prescribing Data
Patients undergoing RFCA for PVC.
Limited benefit from anti-arrhythmic medications; RFCA is preferred.
Clinical Best Practices
Utilize CCTA and radiomics for improved PVC localization. Ensure accurate ECG interpretation by experienced operators. Standardize diagnostic processes for PVC localization. Related Resources & Content
