Abstract：
BACKGROUND: 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.
METHODS: 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.
BACKGROUND: 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.
METHODS: 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.
BACKGROUND: 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.
摘要：
背景：在过去的十年中，已经研究了几种机器学习（ML）算法，以评估其检测语音障碍的功效。文献表明，ML算法可以高精度检测语音障碍。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结果。然而,尽管进行了大量的研究,没有一种算法足够可靠,可用于临床.通过这次审查,我们的目标是通过识别标准音频任务来识别阻碍ML算法在临床环境中使用的关键问题，声学特征,处理算法和影响这些算法有效性的环境因素。
方法：我们将搜索以下数据库：WebofScience,Scopus,Compendex,CINAHL,Medline,IEEE探索和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。文献检索将包括2013年至2023年之间的时期，并且将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。的选择，搜索数据的提取和分析将使用“用于系统审查和Meta分析的首选报告项目扩展”系统进行范围审查。相同的系统也将用于合成结果。
背景：本范围审查不需要伦理批准，因为审查仅由同行评审的出版物组成。研究结果将在与语音病理学相关的同行评审出版物中发表。
方法：我们将搜索以下数据库：WebofScience,Scopus,Compendex,CINAHL,Medline,IEEE探索和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。文献检索将包括2013年至2023年之间的时期，并且将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。的选择，搜索数据的提取和分析将使用“用于系统审查和Meta分析的首选报告项目扩展”系统进行范围审查。相同的系统也将用于合成结果。
背景：本范围审查不需要伦理批准，因为审查仅由同行评审的出版物组成。研究结果将在与语音病理学相关的同行评审出版物中发表。
