Automated prediction of isthmus areas in scar-related atrial tachycardias using artificial intelligence.

INTRODUCTION: Ablation of scar-related reentrant atrial tachycardia (SRRAT) involves identification and ablation of a critical isthmus. A graph convolutional network (GCN) is a machine learning structure that is well-suited to analyze the irregularly-structured data obtained in mapping procedures and may be used to identify potential isthmuses. METHODS: Electroanatomic maps from 29 SRRATs were collected, and custom electrogram features assessing key tissue and wavefront properties were calculated for each point. Isthmuses were labeled off-line. Training data was used to determine the optimal GCN parameters and train the final model. Putative isthmus points were predicted in the training and test populations and grouped into proposed isthmus areas based on density and distance thresholds. The primary outcome was the distance between the centroids of the true and closest proposed isthmus areas. RESULTS: A total of 193 821 points were collected. Thirty isthmuses were detected in 29 tachycardias among 25 patients (median age 65.0, 5 women). The median (IQR) distance between true and the closest proposed isthmus area centroids was 8.2 (3.5, 14.4) mm in the training and 7.3 (2.8, 16.1) mm in the test group. The mean overlap in areas, measured by the Dice coefficient, was 11.5 ± 3.2% in the training group and 13.9 ± 4.6% in the test group. CONCLUSION: A GCN can be trained to identify isthmus areas in SRRATs and may help identify critical ablation targets.

Robust, high-density lesion mapping in the left atrium with near-infrared spectroscopy.

Significance: Radiofrequency ablation (RFA) procedures for atrial fibrillation frequently fail to prevent recurrence, partially due to limitations in assessing extent of ablation. Optical spectroscopy shows promise in assessing RFA lesion formation but has not been validated in conditions resembling those in vivo. Aim: Catheter-based near-infrared spectroscopy (NIRS) was applied to porcine hearts to demonstrate that spectrally derived optical indices remain accurate in blood and at oblique incidence angles. Approach: Porcine left atria were ablated and mapped using a custom-fabricated NIRS catheter. Each atrium was mapped first in phosphate-buffered saline (PBS) then in porcine blood. Results: NIRS measurements showed little angle dependence up to 60 deg. A trained random forest model predicted lesions with a sensitivity of 81.7%, a specificity of 86.1%, and a receiver operating characteristic curve area of 0.921. Predicted lesion maps achieved a mean structural similarity index of 0.749 and a mean normalized inner product of 0.867 when comparing maps obtained in PBS and blood. Conclusions: Catheter-based NIRS can precisely detect RFA lesions on left atria submerged in blood. Optical parameters are reliable in blood and without perpendicular contact, confirming their ability to provide useful feedback during in vivo RFA procedures.