CardioPHON：基于心音图记录的心脏功能筛查的质量评估与自监督预训练
CardioPHON: Quality assessment and self-supervised pretraining for screening of cardiac function based on phonocardiogram recordings
AI总结：
本文提出CardioPHON，一种自监督预训练的心音质量评估与分类工具，可自动去除低质量记录并实现心脏功能异常筛查，在2022年PhysioNet心音挑战赛中多模态模型排名第一、单模态模型在单模态方法中排名第一，是心音领域首个公开发布的预训练模型。
摘要：
心血管疾病的远程监测在早期发现心脏功能异常方面发挥着重要作用，能够实现及时干预、改善预防性护理以及个性化患者治疗。心音中的异常可以通过计算机辅助决策支持系统自动检测，并用作检测心血管问题或监测治疗和干预效果的一线筛查工具。我们在本文中提出了CardioPHON，这是一种集成的心音质量评估和分类工具，可用于从心音图记录中筛查异常心脏功能。该模型以自监督方式在六个中小型心音数据集的集合上进行预训练，能够自动去除低质量记录，以确保心脏异常的细微声音不会被误诊，并在心音分类任务上提供了最先进的性能。结合音频和社会人口学特征的多模态模型表现出卓越的性能，在2022年George B. Moody PhysioNet心音挑战赛的官方排行榜上取得了最佳排名，而仅基于心音图记录的单模态模型在单模态方法中排名第一（总排名第4），超过了利用多种模态的模型。CardioPHON是心音记录领域第一个公开发布的预训练模型，促进了数据高效的人工智能模型的开发，这些模型可以泛化到心血管诊断中的各种下游任务。
英文摘要：
Remote monitoring of cardiovascular diseases plays an essential role in early detection of abnormal cardiac function, enabling timely intervention, improved preventive care, and personalized patient treatment. Abnormalities in the heart sounds can be detected automatically via computer-assisted decision support systems, and used as the first-line screening tool for detection of cardiovascular problems, or for monitoring the effects of treatments and interventions. We propose in this paper CardioPHON, an integrated heart sound quality assessment and classification tool that can be used for screening of abnormal cardiac function from phonocardiogram recordings. The model is pretrained in a self-supervised fashion on a collection of six small- and mid-sized heart sound datasets, enables automatic removal of low quality recordings to ensure that subtle sounds of heart abnormalities are not misdiagnosed, and provides a state-of-the-art performance for the heart sound classification task. The multimodal model that combines audio and socio-demographic features demonstrated superior performance, achieving the best ranking on the official leaderboard of the 2022 George B. Moody PhysioNet heart sound challenge, whereas the unimodal model, that is based only on phonocardiogram recordings, holds the first position among the unimodal approaches (a total rank 4), surpassing the models utilizing multiple modalities. CardioPHON is the first publicly released pretrained model in the domain of heart sound recordings, facilitating the development of data-efficient artificial intelligence models that can generalize to various downstream tasks in cardiovascular diagnostics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
