Optical coherence tomography-enabled classification of the human venoatrial junction

Radiofrequency ablation to treat atrial fibrillation (AF) involves isolating the pulmonary vein from the left atria to prevent AF from occurring. However, creating ablation lesions within the pulmonary veins can cause adverse complications.

We propose automated classification algorithms to classify optical coherence tomography (OCT) volumes of human venoatrial junctions.

A dataset of comprehensive OCT volumes of 26 venoatrial junctions was used for this study. Texture, statistical, and optical features were extracted from OCT patches. Patches were classified as a left atrium or pulmonary vein using random forest (RF), logistic regression (LR), and convolutional neural networks (CNNs). The features were inputs into the RF and LR classifiers. The inputs to the CNNs included: (1) patches and (2) an ensemble of patches and patch-derived features.

Utilizing a sevenfold cross-validation, the patch-only CNN balances sensitivity and specificity best, with an area under the receiver operating characteristic (AUROC) curve of $0.84\pm 0.109$ across the test sets. RF is more sensitive than LR, with an AUROC curve of $0.78\pm 0.102$ .

Cardiac tissues can be identified in benchtop OCT images by automated analysis. Extending this analysis to data obtained in vivo is required to tune automated analysis further. Performing this classification in vivo could aid doctors in identifying substrates of interest and treating AF.