A model-based feature augmentation scheme using biophysical cardiac electrophysiology modeling achieved 97.2% accuracy, 82.4% sensitivity, and 95.0% positive predictive value for RFA target prediction.
Does a model-based feature augmentation scheme improve the machine learning detection of cardiac radio-frequency ablation targets compared to learning from images alone?
Model-based feature augmentation using biophysical cardiac electrophysiology modeling serves as a proof of concept to improve the performance of purely image-driven machine learning for predicting cardiac ablation targets.
GOAL: We present a model-based feature augmentation scheme to improve the performance of a learning algorithm for the detection of cardiac radio-frequency ablation (RFA) targets with respect to learning from images alone. METHODS: Initially, we compute image features from delayed-enhanced magnetic resonance imaging (DE-MRI) to describe local tissue heterogeneities and feed them into a machine learning framework with uncertainty assessment for the identification of potential ablation targets. Next, we introduce the use of a patient-specific image-based model derived from DE-MRI coupled with the Mitchell-Schaeffer electrophysiology model and a dipole formulation for the simulation of intracardiac electrograms. Relevant features are extracted from these simulated signals which serve as a feature augmentation scheme for the learning algorithm. We assess the classifier's performance when using only image features and with model-based feature augmentation. RESULTS: We obtained average classification scores of 97.2 % accuracy, 82.4 % sensitivity, and 95.0 % positive predictive value by using a model-based feature augmentation scheme. Preliminary results also show that training the algorithm on the closest patient from the database, instead of using all the patients, improves the classification results. CONCLUSION: We presented a feature augmentation scheme based on biophysical cardiac electrophysiology modeling to increase the prediction scores of a machine learning framework for the RFA target prediction. SIGNIFICANCE: The results derived from this study are a proof of concept that the use of model-based feature augmentation strengthens the performance of a purely image driven learning scheme for the prediction of cardiac ablation targets.
Lozoya et al. (Thu,) conducted a other in Cardiac radio-frequency ablation (RFA) targets. Model-based feature augmentation scheme vs. Learning from images alone was evaluated on Classification performance for detecting cardiac RFA targets. A model-based feature augmentation scheme using biophysical cardiac electrophysiology modeling achieved 97.2% accuracy, 82.4% sensitivity, and 95.0% positive predictive value for RFA target prediction.