A Deep Reinforcement Learning Approach to Acquire Optimal Fibrillation Ablation Strategies
-
- Tomii Naoki
- Department of Precision Engineering, University of Tokyo, Tokyo, Japan
-
- Seno Hiroshi
- Department of Precision Engineering, University of Tokyo, Tokyo, Japan
-
- Yamazaki Masatoshi
- Medical Device Development and Regulation Research Center, The University of Tokyo, Tokyo, Japan
-
- Sakuma Ichiro
- Medical Device Development and Regulation Research Center, The University of Tokyo, Tokyo, Japan
Bibliographic Information
- Other Title
-
- 最適な細動焼灼戦略の獲得に向けた深層強化学習の試み
Description
<p>Tachyarrhythmias, such as ventricular fibrillation and atrial fibrillation, are caused by abnormal and complex electrical excitation waves in the heart. Ventricular fibrillation is a fatal condition that can lead to sudden cardiac death, and atrial fibrillation increases the risk of stroke due to thrombosis. Today, ablation therapy is widely used to treat fibrillation by ablating the abnormally excited area. However, although various ablation strategies have been proposed, the optimal ablation strategy has not been established. To establish an objective and effective fibrillation ablation strategy, we attempted to construct a machine learning model that selects the optimal ablation target based on the excitation pattern during fibrillation. We report the results of training a deep neural network model that selects the best ablation target based on the time series of the membrane potential distribution, which represents the excitation state of each cell, using a two-dimensional electrophysiological simulation model.</p>
Journal
-
- Transactions of Japanese Society for Medical and Biological Engineering
-
Transactions of Japanese Society for Medical and Biological Engineering Annual59 (Abstract), 147-147, 2021
Japanese Society for Medical and Biological Engineering
- Tweet
Details 詳細情報について
-
- CRID
- 1390008290064417280
-
- NII Article ID
- 130008105096
-
- ISSN
- 18814379
- 1347443X
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
- CiNii Articles
-
- Abstract License Flag
- Disallowed