Automatic WACA Segmentation Using CNN and Random Forest
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Solution Overview
Problem
Conventional methods for wide area circumferential ablation (WACA) in cardiac treatments face limitations due to potential gaps between ablation points and dependence on anatomical structures, leading to pulmonary vein reconnection and recurrent arrhythmia, requiring improved methods for segmentation and contiguity estimation.
Innovation Solution
A system and method utilizing a machine learning algorithm with an evaluation engine that performs automatic segmentation of WACA points using random forest regression, fully connected dense layers, and convolutional neural network (CNN) architecture to classify anatomical structures and predict potential reconnections, reducing the need for manual review and additional ablations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual ablation site anatomical segmentation and manual WACA ablation point contiguity estimation are used, then treatment can be performed, but the process is time-consuming and prone to human error
Solution Approach 1:
The system enables automatic self-assessment of ablation point contiguity and anatomical segmentation through machine learning algorithms, eliminating the need for manual professional evaluation while maintaining or improving accuracy
Solution Approach 2:
Manual mechanical evaluation by medical professionals is replaced with an automated computational system using random forest regression and convolutional neural networks to perform segmentation and contiguity estimation
2Reliability
If WACA ablation is performed point by point, then the procedure can be completed, but gaps may exist between ablation points leading to pulmonary vein reconnection
Solution Approach 1:
The system provides real-time feedback on ablation point contiguity and identifies potential gaps, allowing clinicians to adjust the ablation strategy to ensure complete isolation and prevent pulmonary vein reconnection
Solution Approach 2:
The system performs preliminary assessment of ablation point distribution and predicts potential gaps before they become clinically significant, enabling proactive correction to ensure continuous ablation lines
Data Source
AI summary
A method and apparatus for implementing an evaluation engine implemented using a processor coupled to a memory. The evaluation engine receives effective points respective to cardiac tissue of a patient. The evaluation engine determines an anatomical structural classification for each of the effective points based on a structural segmentation for the cardiac tissue and provides the anatomical structural classification with the each of the plurality of effective points to support treatment of the cardiac tissue.