Evaluation of a fully automated anatomical left atrial mapping pipeline enabled by artficial intelligence

Abstract

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Dr. Fabbricatore and Dr Valeriano have been supported by a research grant provided by the Cardiopath PhD program (University of Naples Federico II) Background Pulmonary vein isolation (PVI) is increasingly performed globally for the treatment of atrial fibrillation [1]. Using a standardized and reproducible workflow, we previously reported an average procedure time of 76.1 ± 26.2 min [2]. Over the last few years, due to workflow optimization and new technological developments, we were able to reduce the skin-to-skin time towards 47.3 ± 7.1 min (unpublished data). Our workflow implements rotational angiography (RA) of the left atrium (LA), which is segmented manually, then converted to a 3D mesh and finally imported into the electroanatomical mapping system (EAM). A second 3D mesh is created by cutting the left bottom anterior part of the LA, including the left atrial appendage, in order to clearly visualize the ridge (Figure 1). Recently, we developed an integrated artificial intelligence (AI) pipeline to automate this process (Figure 2). OBJECTIVE The aim of the study was to evaluate the efficiency of the new AI-based fully automated anatomical mapping of the LA and the efficacy of the automated segmentation. Methods The last 50 PVI procedures that were manually segmented in our lab were processed once more using the AI pipeline. The efficiency of the AI pipeline was assessed by comparing the elapsed time, for both our current manual workflow and the AI pipeline, between the reconstruction of the computed tomography (CT) volume from the 2D RA and the moment both 3D meshes are imported into the EAM. The efficacy of the pipeline was analyzed by calculating the Dice coefficient between the manual and automated segmentation as well as by visually grading segmentation quality on a 4-point Likert scale (1: Bad,2: Sufficient, 3: Good, 4: Excellent) by an experienced cardiac electrophysiologist. Significance testing of timing and visual grading differences between both workflows was performed using a one-sided Wilcoxon signed-rank test. Variables were summarized using median (Q1-Q3). Results The time to segment the LA, create the 3D meshes and import them into the EAM was significantly reduced from 560.0 (513.0–636.5) seconds to 52.0 (49.0–56.0) seconds for the manual and automated workflow respectively (p<0.0001). This reduction is equivalent to about 18% of the current skin-to-skin time for PVI in our lab. Furthermore, we reported a high Dice coefficient between the automated and manual segmentations valued at 0.928 (0.917–0.939). Finally, the subjective quality of the automated segmentation was superior compared to the manual segmentation, with respective values of 4 (3–4) and 3 (3–3.5) (p=0.006). Conclusion We developed an efficient and effective AI pipeline enabling fully automated anatomical mapping of the LA, potentially reducing our current PVI skin-to-skin time by 18%. The robustness of the AI segmentation model has been validated objectively (Dice coefficient) and subjectively. The efficacy of the AI cut model, the second model in the pipeline, remains to be verified.