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Machine-Learning-Based Multi-Modal Force Estimation for Steerable Ablation Catheters
E Arefinia1, J Jayender2, R V Patel1
1Department of Electrical and Computer Engineering, Western University, London, ON, Canada, and Canadian Surgical Technologies and Advanced Robotics (CSTAR), University Hospital, LHSC, London, ON, Canada.
IEEE Transactions on Medical Robotics and Bionics
|September 16, 2024
Summary
This study introduces a novel AI-based system for estimating catheter contact force during cardiac ablation procedures. The multi-modal approach significantly improves accuracy, enhancing patient safety in atrial fibrillation treatments.
Area of Science:
- Cardiovascular Medicine
- Biomedical Engineering
- Artificial Intelligence in Healthcare
Background:
- Catheter-based cardiac ablation is crucial for treating atrial fibrillation (AF).
- Accurate contact force measurement between the catheter and heart tissue is vital for effective lesion creation and procedural success.
- Current methods may lack precision, necessitating improved sensing technologies.
Purpose of the Study:
- To develop and validate a novel multi-modal contact force estimator for catheter-based cardiac ablation.
- To leverage deep learning, specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), for enhanced force estimation.
- To assess the estimator's performance using simulated fluoroscopy and real-world data acquisition.
Main Methods:
- A multi-modal approach using catheter shape and optical flow from video data.
- Feature extraction via transfer learning from image and optical flow modalities.
- Integration of Long Short-Term Memory Networks (LSTMs) with a memory fusion network (MFN) to capture temporal dependencies and hysteresis.
- Spatial and temporal network architecture with late fusion techniques (concatenation of LSTMs, transformer decoders, GRUs).
Main Results:
- The proposed multi-modal network achieved a mean absolute error of only 2.84% of the total magnitude.
- Performance was superior to single-modality networks and basic late fusion concatenation.
- Data collected under realistic conditions demonstrated the estimator's practicality.
Conclusions:
- The developed multi-modal AI-based contact force estimator is highly accurate and practical for cardiac ablation.
- This sensor-free approach offers a significant improvement over existing methods.
- The findings highlight the potential of multimodal deep learning in advancing minimally invasive cardiac procedures.
