Identification of Catheter Ablation Sites Using Patient-Specific CARTO Coordinate Data

The global increase in population poses challenges in aging societies. As the number of atrial fibrillation (AF) cases is expected to rise due to this demographic shift, the need for efficient treatment methods is growing. This study aims to partially automate the operation of 3D mapping systems used in AF treatment with the CARTO system. We analyzed X, Y, and Z coordinate data labeled as LPV, RPV, and CTI, which were extracted from the CARTO system. A dataset comprising 10 cases was used for analysis. We defined a reference point at the LPV rooftop and calculated the Euclidean distance to each coordinate. We then compared two datasets: one containing only X, Y, and Z coordinates, and another including both coordinates and distance. First, we visualized the data using principal component analysis (PCA). Next, we evaluated the classification accuracy of four models: k-Nearest Neighbors, Random Forest, SGD Classifier, and Linear SVC. Incorporating distance data reduced the overlap of LPV, RPV, and CTI in the PCA visualization. All classification models showed significant improvements in test and training accuracy, precision, recall, and F1 score when distance data was included.