Sasaki Wataru, Tanaka Naomichi, Matsumoto Kazuhisa, Kawano Daisuke, Narita Masataka, Naganuma Tsukasa, Tsutsui Kenta, Mori Hitoshi, Ikeda Yoshifumi, Arai Takahide, Matsumoto Kazuo, Kato Ritsushi
Department of Cardiology Saitama Medical University, International Medical Center Hidaka Saitama Japan.
Department of Cardiology Higashimatsuyama Medical Association Hospital Higashimatsuyama Saitama Japan.
J Arrhythm. 2024 Aug 13;40(5):1085-1092. doi: 10.1002/joa3.13131. eCollection 2024 Oct.
CARTONET® enables automatic ablation site classification and reconnection site prediction using machine learning. However, the accuracy of the site classification model and trends of the site prediction model for potential reconnection sites are uncertain.
We studied a total of 396 cases. About 313 patients underwent pulmonary vein isolation (PVI), including a cavotricuspid isthmus (CTI) ablation (PVI group) and 83 underwent PVI and additional ablation (i.e., box isolation) (PVI+ group). We investigated the sensitivity and positive predictive value (PPV) for automatic site classification in the total cohort and compared these metrics for PV lesions versus non-PV lesions. The distribution of potential reconnection sites and confidence level for each site was also investigated.
A total of 29,422 points were analyzed (PV lesions [ = 22 418], non-PV lesions [ = 7004]). The sensitivity and PPV of the total cohort were 71.4% and 84.6%, respectively. The sensitivity and PPV of PV lesions were significantly higher than those of non-PV lesions (PV lesions vs. non-PV lesions, %; sensitivity, 75.3 vs. 67.5, < .05; PPV, 91.2 vs. 67.9, < .05). CTI and superior vena cava could not be recognized or analyzed. In the potential reconnection prediction model, the incidence of potential reconnections was highest in the posterior, while the confidence was the highest in the roof.
The automatic site classification of the CARTONET®R12.1 model demonstrates relatively high accuracy in pulmonary veins excluding the carina. The prediction of potential reconnection sites feature tends to anticipate areas with poor catheter stability as reconnection sites.
CARTONET®可利用机器学习实现消融部位的自动分类和重新连接部位的预测。然而，用于潜在重新连接部位的部位分类模型的准确性以及部位预测模型的趋势尚不确定。
我们共研究了396例病例。约313例患者接受了肺静脉隔离（PVI），包括三尖瓣峡部（CTI）消融（PVI组），83例接受了PVI及额外消融（即盒状隔离）（PVI+组）。我们调查了整个队列中自动部位分类的敏感性和阳性预测值（PPV），并比较了肺静脉病变与非肺静脉病变的这些指标。还研究了潜在重新连接部位的分布以及每个部位的置信水平。
共分析了29422个点（肺静脉病变[=22418]，非肺静脉病变[=7004]）。整个队列的敏感性和PPV分别为71.4%和84.6%。肺静脉病变的敏感性和PPV显著高于非肺静脉病变（肺静脉病变与非肺静脉病变，%；敏感性，75.3对67.5，<.05；PPV，91.2对67.9，<.05）。CTI和上腔静脉无法识别或分析。在潜在重新连接预测模型中，潜在重新连接的发生率在后部最高，而置信度在顶部最高。
CARTONET®R12.1模型的自动部位分类在不包括隆突的肺静脉中显示出相对较高的准确性。潜在重新连接部位的预测特征倾向于将导管稳定性差的区域预测为重新连接部位。
