Department of Cardiovascular Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN.
Division of Otolaryngology, Mayo Clinic College of Medicine and Science, Rochester, MN; Chaim Sheba Medical Center, Tel HaShomer, Israel.
Mayo Clin Proc. 2023 Sep;98(9):1353-1375. doi: 10.1016/j.mayocp.2023.03.007. Epub 2023 Mar 28.
The advancement of digital biomarkers and the provision of remote health care greatly progressed during the coronavirus disease 2019 global pandemic. Combining voice/speech data with artificial intelligence and machine-based learning offers a novel solution to the growing demand for telemedicine. Voice biomarkers, obtained from the extraction of characteristic acoustic and linguistic features, are associated with a variety of diseases and even coronavirus disease 2019. In the current review, we (1) describe the basis on which digital voice biomarkers could facilitate "telemedicine," (2) discuss potential mechanisms that may explain the association between voice biomarkers and disease, (3) offer a novel classification system to conceptualize voice biomarkers depending on different methods for recording and analyzing voice/speech samples, (4) outline evidence revealing an association between voice biomarkers and a number of disease states, and (5) describe the process of developing a voice biomarker from recording, storing voice samples, and extracting acoustic and linguistic features relevant to training and testing deep and machine-based learning algorithms to detect disease. We further explore several important future considerations in this area of research, including the necessity for clinical trials and the importance of safeguarding data and individual privacy. To this end, we searched PubMed and Google Scholar to identify studies evaluating the relationship between voice/speech features and biomarkers and various diseases. Search terms included digital biomarker, telemedicine, voice features, voice biomarker, speech features, speech biomarkers, acoustics, linguistics, cardiovascular disease, neurologic disease, psychiatric disease, and infectious disease. The search was limited to studies published in English in peer-reviewed journals between 1980 and the present. To identify potential studies not captured by our database search strategy, we also searched studies listed in the bibliography of relevant publications and reviews.
数字生物标志物的进步和远程医疗的提供在 2019 年冠状病毒病全球大流行期间取得了巨大进展。将语音/言语数据与人工智能和基于机器的学习相结合,为日益增长的远程医疗需求提供了一种新的解决方案。从提取特征声学和语言特征中获得的语音生物标志物与多种疾病甚至 2019 年冠状病毒病有关。在当前的综述中,我们:(1)描述了数字语音生物标志物如何促进“远程医疗”的基础;(2)讨论了可能解释语音生物标志物与疾病之间关联的潜在机制;(3)提供了一种新的分类系统,根据记录和分析语音/言语样本的不同方法来概念化语音生物标志物;(4)概述了揭示语音生物标志物与许多疾病状态之间存在关联的证据;(5)描述了从记录、存储语音样本和提取与训练和测试深度学习和基于机器的算法相关的声学和语言特征中开发语音生物标志物的过程,以检测疾病。我们还进一步探讨了该研究领域的几个重要未来考虑因素,包括临床试验的必要性以及保护数据和个人隐私的重要性。为此,我们在 PubMed 和 Google Scholar 上搜索了评估语音/言语特征与生物标志物和各种疾病之间关系的研究。搜索词包括数字生物标志物、远程医疗、语音特征、语音生物标志物、言语特征、言语生物标志物、声学、语言学、心血管疾病、神经疾病、精神疾病和传染病。搜索仅限于 1980 年至现在在同行评议期刊上发表的英语研究。为了识别我们的数据库搜索策略未捕获的潜在研究,我们还搜索了相关出版物和评论的参考书目列出的研究。