Model-Based Feature Augmentation for Cardiac Ablation Target Learning From Images

• Published in: IEEE transactions on biomedical engineering Vol. 66; no. 1; pp. 30 - 40
• Main Authors: Lozoya, Rocio Cabrera, Berte, Benjamin, Cochet, Hubert, Jais, Pierre, Ayache, Nicholas, Sermesant, Maxime
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Ablation; Algorithms; Artificial Intelligence; Augmentation; Biological system modeling; cardiac electrophysiology modelling; Catheter Ablation - methods; Catheters; Classification; Computer Science; Computer simulation; Electric potential; electroanatomical mapping; Electrophysiology; Engineering Sciences; Feature extraction; Heart; Heart - diagnostic imaging; Heart - physiopathology; Humans; Image classification; Image enhancement; Image Interpretation, Computer-Assisted - methods; intracardiac electrogram modelling; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical models; Mathematics; Medical Imaging; Models, Cardiovascular; NMR; Nuclear magnetic resonance; Performance enhancement; Physics; Predictions; Radio-frequency ablation planning; Sensitivity and Specificity; Signal and Image Processing; Target recognition; trophysiology modelling; electroanatomical mapping; Index Terms—radio-frequency ablation planning; intracardiac electrogram modelling; cardiac elec
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2018.2818300

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.