导管消融(CA)被认为是根除持续性和异常心律失常的最有效方法之一。然而,在某些情况下,这些心律失常没有得到适当的治疗,导致它们复发。如果不及时治疗,它们可能会导致中风等并发症,心力衰竭,或死亡。直到最近,诊断CA后复发性心律失常的主要技术是在随访期间对心脏影像学和心电图上的心律失常引起的变化易感的发现。或患者在消融后报告心悸或胸部不适。然而,这些后续行动可能既耗时又昂贵,他们可能并不总是确定复发的根本原因。随着人工智能(AI)的引入,这些后续访问可以有效缩短,和改进的方法来预测他们的消融程序后复发心律失常的可能性可以开发。AI可以分为两类:机器学习(ML)和深度学习(DL),后者是ML的子集。ML和DL模型已在多项研究中使用,以证明其使用临床变量预测和识别心律失常的能力。电生理特性,和从成像数据中提取的趋势。由于AI能够计算大量数据并检测电信号和心脏图像的细微变化,因此已被证明是心脏病专家的宝贵帮助。这可能会增加CA后复发性心律失常的风险。尽管这些涉及人工智能的研究产生了与人类干预相当或优于人类干预的有希望的结果,他们主要关注心房颤动,而房扑(AFL)和房性心动过速(AT)是相对较少的AI研究.因此,这篇综述的目的是研究人工智能算法的相互作用,电生理特性,成像数据,风险评分计算器,和临床变量在预测消融术后的心律失常。这篇评论还将讨论这些算法的实现,以实现CA后AFL和AT复发的检测和预测。
Catheter ablation (CA) is considered as one of the most effective methods technique for eradicating persistent and abnormal cardiac arrhythmias. Nevertheless, in some cases, these arrhythmias are not treated properly, resulting in their recurrences. If left untreated, they may result in complications such as strokes, heart failure, or death. Until recently, the primary techniques for diagnosing recurrent arrhythmias following CA were the findings predisposing to the changes caused by the arrhythmias on cardiac imaging and electrocardiograms during follow-up visits, or if patients reported having palpitations or chest discomfort after the ablation. However, these follow-ups may be time-consuming and costly, and they may not always determine the root cause of the recurrences. With the introduction of artificial intelligence (AI), these follow-up visits can be effectively shortened, and improved methods for predicting the likelihood of recurring arrhythmias after their ablation procedures can be developed. AI can be divided into two categories: machine learning (ML) and deep learning (DL), the latter of which is a subset of ML. ML and DL models have been used in several studies to demonstrate their ability to predict and identify cardiac arrhythmias using clinical variables, electrophysiological characteristics, and trends extracted from imaging data. AI has proven to be a valuable aid for cardiologists due to its ability to compute massive amounts of data and detect subtle changes in electric signals and cardiac images, which may potentially increase the risk of recurrent arrhythmias after CA. Despite the fact that these studies involving AI have generated promising outcomes comparable to or superior to human intervention, they have primarily focused on atrial fibrillation while atrial flutter (AFL) and atrial tachycardia (AT) were the subjects of relatively few AI studies. Therefore, the aim of this review is to investigate the interaction of AI algorithms, electrophysiological characteristics, imaging data, risk score calculators, and clinical variables in predicting cardiac arrhythmias following an ablation procedure. This review will also discuss the implementation of these algorithms to enable the detection and prediction of AFL and AT recurrences following CA.