Recommender system for ablation lines to treat complex atrial tachycardia. (April 2023) - Vila, Muhamed; Rivolta, Massimo W.; Barrios Espinosa, Cristian A.; Unger, Laura A.; Luik, Armin; Loewe, Axel; Sassi, Roberto
Highlights: We present the first recommender system for the identification of the optimal ablation lines for complex atrial tachycardias, based solely on data collected during the intervention. The system was designed using graph theory and the ablation strategy is the solution of an optimization problem. An efficient heuristic-based algorithm was specifically designed and implemented to find the solution of the optimization problem. For the mechanisms considered, the ablation strategies identified by the recommender system matched the ones usually planned in clinical practice, and partially matched with those actually performed. Abstract: Background and Objective: Planning the optimal ablation strategy for the treatment of complex atrial tachycardia (CAT) is a time consuming task and is error-prone. Recently, directed network mapping, a technology based on graph theory, proved to efficiently identify CAT based solely on data of clinical interventions. Briefly, a directed network was used to model the atrial electrical propagation and reentrant activities were identified by looking for closed-loop paths in the network. In this study, we propose a recommender system, built as an optimization problem, able to suggest the optimal ablation strategy for the treatment of CAT. Methods: The optimization problem modeled the optimal ablation strategy as that one interrupting all reentrant mechanisms while minimizing the ablated atrial surface. The problem was designed on top of directed network mapping. Considering the exponential complexity of finding the optimal solution of the problem, we introduced a heuristic algorithm with polynomial complexity. The proposed algorithm was applied to the data of i) 6 simulated scenarios including both left and right atrial flutter; and ii) 10 subjects that underwent a clinical routine. Results: The recommender system suggested the optimal strategy in 4 out of 6 simulated scenarios. On clinical data, the recommended ablation lines were found satisfactory on 67% of the cases according to the clinician's opinion, while they were correctly located in 89%. The algorithm made use of only data collected during mapping and was able to process them nearly real-time. Conclusions: The first recommender system for the identification of the optimal ablation lines for CAT, based solely on the data collected during the intervention, is presented. The study may open up interesting scenarios for the application of graph theory for the treatment of CAT.