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人工智能时代瓣膜性和先天性心脏病的听力学诊断

Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence.

作者信息

Ainiwaer Aikeliyaer, Kadier Kaisaierjiang, Qin Lian, Rehemuding Rena, Ma Xiang, Ma Yi-Tong

机构信息

Department of Cardiology, Xinjiang Medical University Affiliated First Hospital, 830011 Urumqi, Xinjiang, China.

出版信息

Rev Cardiovasc Med. 2023 Jun 14;24(6):175. doi: 10.31083/j.rcm2406175. eCollection 2023 Jun.

Abstract

In recent years, electronic stethoscopes have been combined with artificial intelligence (AI) technology to digitally acquire heart sounds, intelligently identify valvular disease and congenital heart disease, and improve the accuracy of heart disease diagnosis. The research on AI-based intelligent stethoscopy technology mainly focuses on AI algorithms, and the commonly used methods are end-to-end deep learning algorithms and machine learning algorithms based on feature extraction, and the hot spot for future research is to establish a large standardized heart sound database and unify these algorithms for external validation; in addition, different electronic stethoscopes should also be extensively compared so that the algorithms can be compatible with different. In addition, there should be extensive comparison of different electronic stethoscopes so that the algorithms can be compatible with heart sounds collected by different stethoscopes; especially importantly, the deployment of algorithms in the cloud is a major trend in the future development of artificial intelligence. Finally, the research of artificial intelligence based on heart sounds is still in the preliminary stage, although there is great progress in identifying valve disease and congenital heart disease, they are all in the research of algorithm for disease diagnosis, and there is little research on disease severity, remote monitoring, prognosis, etc., which will be a hot spot for future research.

摘要

近年来,电子听诊器已与人工智能(AI)技术相结合,用于数字化采集心音、智能识别瓣膜疾病和先天性心脏病,并提高心脏病诊断的准确性。基于AI的智能听诊技术研究主要集中在AI算法上,常用方法是基于特征提取的端到端深度学习算法和机器学习算法,未来研究的热点是建立大型标准化心音数据库并统一这些算法进行外部验证;此外,还应广泛比较不同的电子听诊器,以使算法能够与不同的听诊器兼容。尤其重要的是,算法在云端的部署是人工智能未来发展的一个主要趋势。最后,基于心音的人工智能研究仍处于初步阶段,虽然在识别瓣膜疾病和先天性心脏病方面有很大进展,但这些都还处于疾病诊断算法的研究中,对于疾病严重程度、远程监测、预后等方面的研究较少,这将是未来研究的一个热点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4aa0/11264159/14aa69fb5367/2153-8174-24-6-175-g1.jpg

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