📄 中文摘要
高密度映射技术和术前CT/MRI在定位心律失常方面仍然耗时且资源密集。AI已被验证为临床决策辅助工具，能够提供准确、快速的超声图像实时分析。基于此，提出了一种AI驱动的框架，利用心内超声（ICE），作为电生理程序的常规部分，引导临床医生识别心律失常发生区域，并可能减少手术时间。心律失常源定位被构建为三类分类任务，基于ICE视频数据区分正常窦性心律、左侧和右侧心律失常。开发了一种3D卷积神经网络，旨在区分这三类心律失常。
VISION-ICE：基于视频的心内超声心律失常起源的解读与空间识别
📄 English Summary
VISION-ICE: Video-based Interpretation and Spatial Identification of Arrhythmia Origins via Neural Networks in Intracardiac Echocardiography
Contemporary high-density mapping techniques and preoperative CT/MRI remain time and resource intensive in localizing arrhythmias. AI has been validated as a clinical decision aid in providing accurate, rapid real-time analysis of echocardiographic images. An AI-enabled framework is proposed that leverages intracardiac echocardiography (ICE), a routine part of electrophysiology procedures, to guide clinicians toward areas of arrhythmogenesis and potentially reduce procedural time. Arrhythmia source localization is formulated as a three-class classification task, distinguishing normal sinus rhythm, left-sided, and right-sided arrhythmias, based on ICE video data. A 3D Convolutional Neural Network has been developed to discriminate these three types of arrhythmias.