School of Electrical and Information Engineering, The University of Sydney Faculty of Engineering and Information Technologies, Sydney, New South Wales, Australia.
Sydney School of Health Sciences, The University of Sydney Faculty of Medicine and Health, Sydney, New South Wales, Australia.
BMJ Open. 2024 Feb 24;14(2):e076998. doi: 10.1136/bmjopen-2023-076998.
Over the past decade, several machine learning (ML) algorithms have been investigated to assess their efficacy in detecting voice disorders. Literature indicates that ML algorithms can detect voice disorders with high accuracy. This suggests that ML has the potential to assist clinicians in the analysis and treatment outcome evaluation of voice disorders. However, despite numerous research studies, none of the algorithms have been sufficiently reliable to be used in clinical settings. Through this review, we aim to identify critical issues that have inhibited the use of ML algorithms in clinical settings by identifying standard audio tasks, acoustic features, processing algorithms and environmental factors that affect the efficacy of those algorithms.
We will search the following databases: Web of Science, Scopus, Compendex, CINAHL, Medline, IEEE Explore and Embase. Our search strategy has been developed with the assistance of the university library staff to accommodate the different syntactical requirements. The literature search will include the period between 2013 and 2023, and will be confined to articles published in English. We will exclude editorials, ongoing studies and working papers. The selection, extraction and analysis of the search data will be conducted using the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews' system. The same system will also be used for the synthesis of the results.
This scoping review does not require ethics approval as the review solely consists of peer-reviewed publications. The findings will be presented in peer-reviewed publications related to voice pathology.
在过去的十年中，已经研究了几种机器学习（ML）算法，以评估它们在检测语音障碍方面的效果。文献表明，ML 算法可以高精度地检测语音障碍。这表明 ML 有可能帮助临床医生分析和评估语音障碍的治疗效果。然而，尽管有许多研究，但没有一种算法足够可靠，无法在临床环境中使用。通过本次综述，我们旨在通过确定影响这些算法效果的标准音频任务、声学特征、处理算法和环境因素，确定阻碍 ML 算法在临床环境中使用的关键问题。
我们将搜索以下数据库：Web of Science、Scopus、Compendex、CINAHL、Medline、IEEE Explore 和 Embase。我们的搜索策略是在大学图书馆工作人员的协助下制定的，以适应不同的语法要求。文献搜索将包括 2013 年至 2023 年期间，并仅限于以英语发表的文章。我们将排除社论、正在进行的研究和工作文件。使用“系统评价和荟萃分析扩展的首选报告项目”系统进行搜索数据的选择、提取和分析。同样的系统也将用于结果的综合。
由于本次综述仅包含同行评审的出版物，因此不需要伦理批准。研究结果将在与语音病理学相关的同行评审出版物中呈现。
