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cAPM: Continual AI-assisted pace-mapping with active learning
Dylan O'Hara1, Pradeep Bajracharya1, Casey Meisenzahl1
1Rochester Institute of Technology, Rochester, New York, USA.
Computers in Biology and Medicine
|July 30, 2026
Summary
Continuous AI-assisted pace-mapping (cAPM) significantly improves ventricular tachycardia (VT) ablation by transferring knowledge across procedures. This AI approach reduces the number of pacing sites needed for accurate VT target localization.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Ventricular tachycardia (VT) is a critical cause of sudden cardiac death.
- Pace-mapping guides catheter ablation by analyzing ventricular pacing sites and ECGs.
- Current AI methods for pace-mapping lack knowledge transfer capabilities across different VT cases.
Purpose of the Study:
- To introduce cAPM, a continuous AI-assisted pace-mapping system.
- To enable knowledge transfer from past pace-mapping data for future VT localizations.
- To reduce the data required for efficient and accurate VT target identification.
Main Methods:
- Developed a task-agnostic surrogate neural network mapping pacing sites to 12-lead ECG morphology.
- Implemented an active-learning strategy for selecting informative pacing sites.
- Utilized a continual learning strategy to sequentially update the model and retain prior knowledge.
Main Results:
- cAPM achieved an 82% probability of localizing VT targets within 5 mm accuracy using only 4.5 pacing sites.
- This significantly outperforms the state-of-the-art active-learning method, which required 12.6 pacing sites for 45% accuracy.
- Performance was consistent across diverse simulated physiological conditions and ventricular geometries.
Conclusions:
- cAPM demonstrates a novel approach for continuous, knowledge-transferring AI in pace-mapping.
- The system substantially enhances the efficiency and accuracy of VT target localization.
- Results support further development for prospective in-vivo preclinical and clinical studies.
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