Machine Learning in Atrial Fibrillation

心房颤动中的机器学习

• 批准号: 10347364
• 负责人: Sanjiv M Narayan
• 金额: $ 74.91万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10347364
• 关键词: Ablation; Action Potentials; Acute; Address; Affect; Algorithms; American; Anatomy; Area; Arrhythmia; Atrial Fibrillation; Biological; Biometry; Biostatistical Methods; Cessation of life; Clinical; Clinical Data; Complex; Computer Models; Data; Data Element; Data Set; Diagnosis; Disease; Dizziness; Drug Controls; Electrophysiology (science); Foundations; Funding; Genetic; Heart; Heart Atrium; Heart failure; Hospitalization; Human; Image Analysis; Individual; Label; Lesion; Link; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Methods; Noise; Outcome; Patient Care; Patients; Pattern; Persons; Phenotype; Physics; Physiological; Play; Public Health; Recovery; Registries; Science; Signal Transduction; Site; Stroke; Test Result; Testing; Tissues; Training; Uncertainty; United States National Institutes of Health; Work; base; computer science; convolutional neural network; demographics; digital; experience; heart rhythm; improved; individual patient; individualized medicine; insight; machine learning classifier; machine learning method; mortality; novel; personalized medicine; personalized strategies; prospective; prospective test; response; signal processing; success; tool; translational impact; voice recognition; voltage

Project Summary Atrial fibrillation (AF) is the most common heart rhythm disorder, affecting 2 million Americans in whom it may cause skipped heart beats, dizziness or stroke. Unfortunately, therapy for AF has limited success, likely because AF represents heterogenous and poorly characterized disease entities between individuals. A central challenge is that it is not clear why a specific therapy works in a given AF patient. This uncertainty makes it challenging to develop a patient-specific approach to tailor therapy for personalized medicine. The premise of this project is that mechanistic data is increasingly available in AF patients at scales spanning tissue, whole heart and patient levels, yet rarely integrated. We set out to use machine learning (ML), a powerful approach proven to classify complex datasets, to integrate data to address 3 clinical unmet needs. First, electrograms are rarely used to guide therapy in AF, unlike organized rhythms, because they are difficult to interpret. Second, it is difficult to understand how arrhythmia is affected by any specific ablation strategy in AF, unlike organized rhythms. This makes it difficult to improve therapy. Third, it is difficult to identify whether an individual patient will or will not have success from AF ablation. We applied machine learning and novel objective analyses to these questions to develop strategies for personalized AF therapy. We have 3 specific aims: (1) To identify components of AF electrograms using ML trained to monophasic action potentials (MAP); (2) To identify electrical and structural features of the acute response of AF to ablation near and remote from PVs; (3) To identify patients in whom ablation is unsuccessful or successful long-term, who are poorly separated at present. Each Aim will compare ML to traditional biostatistics, and use objective explainability analysis of ML to provide mechanistic insights. This study has potential to deliver immediate clinical and translational impact. We will apply specific ML approaches, biostatistics, and computer modeling to our rich multiscale registry. We will develop practical and shareable tools, which we will prospectively test clinically, to deliver meaningful outcomes at tissue, whole heart and patient scales. Our team is experienced in electrophysiology, computer science, signal processing and biological physics. This project is likely to reveal novel multiscale AF phenotypes to enable personalized therapy.

项目摘要 心房颤动（AF）是最常见的心律障碍，影响了200万美国人 它可能会导致跳过心跳，头晕或中风。不幸的是，对AF的治疗有限 成功，可能是因为AF代表异源性和特征性疾病实体 个人。一个核心挑战是，尚不清楚为什么特定疗法在给定的AF患者中起作用。 这种不确定性使得开发一种特定于患者的方法来裁缝治疗的挑战 个性化医学。 该项目的前提是，AF患者的机械数据越来越多 范围跨越组织，整个心脏和患者水平，但很少融合。我们着手使用机器 学习（ML）是一种对复杂数据集进行分类的强大方法，将数据集成到地址3 临床未满足的需求。首先，与有组织不同 节奏，因为它们很难解释。其次，很难理解心律不齐 与有组织的节奏不同，受AF中任何特定消融策略的影响。这使得很难 改善治疗。第三，很难确定单个患者是否会成功 从AF消融。我们将机器学习和新颖的客观分析应用于这些问题 制定个性化AF疗法的策略。 我们有3个具体目标：（1）使用经过训练的ML识别AF电电的组件 单质作用电位（地图）； （2）识别急性的电气和结构特征 AF对靠近PV的消融的响应； （3）确定消融的患者 目前没有成功的长期或成功的长期。每个目标都将ML与 传统的生物统计学，并利用ML的客观解释性分析来提供机械见解。 这项研究有可能产生立即的临床和翻译影响。我们将申请 特定的ML方法，生物统计学和计算机建模为我们丰富的多尺度注册表。我们将 开发实用和可共享的工具，我们将在临床上进行前瞻性测试，以提供有意义的 在组织，心脏和患者尺度上的结果。我们的团队在电生理学方面经验丰富， 计算机科学，信号处理和生物物理学。这个项目可能会揭示小说 多尺度AF表型可实现个性化治疗。