Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.
Exp Biol Med (Maywood). 2022 Nov;247(22):2053-2061. doi: 10.1177/15353702221115428. Epub 2022 Aug 16.
Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is going on in this field, in this narrative review, we describe publicly available audio databases that can be used for experiments, illustrate the developed ML methods proposed to date, and flag some under-considered issues which still need attention. Compared to existing surveys on the topic, we cover the latest literature, especially those audio-based COVID-19 detection studies which have gained extensive attention in the last two years. This work can help to facilitate the application of artificial intelligence in the respiratory auscultation field.
听诊在临床中起着重要作用，研究界一直在探索机器学习 (ML)，以通过声音实现远程和自动听诊，从而进行呼吸状况筛查。为了全面了解该领域的现状，在本篇叙述性综述中，我们描述了可用于实验的公开音频数据库，举例说明了迄今为止提出的开发的 ML 方法，并指出了一些仍需要关注的未被充分考虑的问题。与该主题的现有调查相比，我们涵盖了最新的文献，特别是那些在过去两年中引起广泛关注的基于音频的 COVID-19 检测研究。这项工作有助于促进人工智能在呼吸听诊领域的应用。
