Comparison of the characteristics between machine learning and deep learning algorithms for ablation site classification in a novel cloud-based system.

Abstract

Background: CARTONET is a cloud-based system for the analysis of ablation procedures using the CARTO system. The current CARTONET R14 model employs deep learning, but its accuracy and positive predictive value (PPV) remain under-evaluated.

Objective: This study aimed to compare the characteristics of the CARTONET system between the R12.1 and the R14 models.

Methods: Data from 396 atrial fibrillation ablation cases were analyzed. Using a CARTONET R14 model, the sensitivity and PPV of the automated anatomical location model were investigated. The distribution of potential reconnection sites and confidence level for each site were investigated. We also compared the difference in that data between the CARTONET R12.1, the previous CARTONET version, and the CARTONET R14 models.

Results: We analyzed the overall tags of 39169 points and the gap prediction of 625 segments using the CARTONET R14 model. The sensitivity and PPV of the R14 model significantly improved compared to that of the R12.1 model (R12.1 vs. R14; sensitivity, 71.2% vs. 77.5%, p<0.0001; PPV, 85.6 % vs. 86.2 %, p=0.0184). The incidence of reconnections was highly observed in the posterior area of the RPVs and LPVs (RPV, 98/238 [41.2%]; LPV 190/387 [49.1%]). On the other hand, the possibility of reconnection was highest in the roof area for the RPVs and LPVs (%; RPV, 14 [5.5-41]; LPV, 16 [8-22]).

Conclusion: The R14 model significantly improved sensitivity and PPV compared to the R12.1 model. The tendency for predicting potential reconnection sites was similar to the previous version, the R12 model.