{"title":"在过去五年中，人工智能在语音障碍诊断和管理中的全球应用","authors":"Amna Suleman, Amy L. Rutt","doi":"10.1002/eer3.70006","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>This review evaluates the worldwide use of artificial intelligence (AI) for the diagnosis and treatment of voice disorders.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>An electronic search was completed in Embase, Pubmed, Ovid MEDLINE, Scopus, Google Scholar, and Web of Science. Studies in English from 2019 to 2024 evaluating the use of AI in detection and management of voice disorders were included. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Eighty-one studies were recognized. Thirty-three studies were chosen and screened for quality assessment. Of these, 16 studies used AI to determine normal versus pathological voice. The convolutional neural network (CNN) was the most employed algorithm among all machine learning algorithms.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>This review revealed significant interest worldwide in utilizing AI in detection of voice disorders. Gaps included the use of limited, inconsistent data sets, lack of validation, and emphasis on detection rather than treatment of the voice disorder. These are areas of opportunity for AI techniques to improved diagnostic accuracy.</p>\n </section>\n </div>","PeriodicalId":100519,"journal":{"name":"Eye & ENT Research","volume":"2 2","pages":"88-95"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eer3.70006","citationCount":"0","resultStr":"{\"title\":\"Global utilization of artificial intelligence in the diagnosis and management of voice disorders over the past five years\",\"authors\":\"Amna Suleman, Amy L. Rutt\",\"doi\":\"10.1002/eer3.70006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>This review evaluates the worldwide use of artificial intelligence (AI) for the diagnosis and treatment of voice disorders.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>An electronic search was completed in Embase, Pubmed, Ovid MEDLINE, Scopus, Google Scholar, and Web of Science. Studies in English from 2019 to 2024 evaluating the use of AI in detection and management of voice disorders were included. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Eighty-one studies were recognized. Thirty-three studies were chosen and screened for quality assessment. Of these, 16 studies used AI to determine normal versus pathological voice. The convolutional neural network (CNN) was the most employed algorithm among all machine learning algorithms.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>This review revealed significant interest worldwide in utilizing AI in detection of voice disorders. Gaps included the use of limited, inconsistent data sets, lack of validation, and emphasis on detection rather than treatment of the voice disorder. These are areas of opportunity for AI techniques to improved diagnostic accuracy.</p>\\n </section>\\n </div>\",\"PeriodicalId\":100519,\"journal\":{\"name\":\"Eye & ENT Research\",\"volume\":\"2 2\",\"pages\":\"88-95\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eer3.70006\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Eye & ENT Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/eer3.70006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Eye & ENT Research","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/eer3.70006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
目的综述人工智能（AI）在语音障碍诊断和治疗中的应用。方法在Embase、Pubmed、Ovid MEDLINE、Scopus、谷歌Scholar和Web of Science中进行电子检索。纳入了2019年至2024年的英语研究，评估了人工智能在语音障碍检测和管理中的应用。遵循系统评价和荟萃分析指南的首选报告项目。结果共确认81项研究。选择33项研究进行质量评估。其中，有16项研究使用人工智能来确定正常和病理的声音。卷积神经网络（CNN）是所有机器学习算法中使用最多的算法。结论本综述揭示了世界范围内利用人工智能检测语音障碍的重大兴趣。差距包括使用有限，不一致的数据集，缺乏验证，强调检测而不是治疗语音障碍。这些都是人工智能技术提高诊断准确性的机会。
Global utilization of artificial intelligence in the diagnosis and management of voice disorders over the past five years
Objective
This review evaluates the worldwide use of artificial intelligence (AI) for the diagnosis and treatment of voice disorders.
Methods
An electronic search was completed in Embase, Pubmed, Ovid MEDLINE, Scopus, Google Scholar, and Web of Science. Studies in English from 2019 to 2024 evaluating the use of AI in detection and management of voice disorders were included. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed.
Results
Eighty-one studies were recognized. Thirty-three studies were chosen and screened for quality assessment. Of these, 16 studies used AI to determine normal versus pathological voice. The convolutional neural network (CNN) was the most employed algorithm among all machine learning algorithms.
Conclusion
This review revealed significant interest worldwide in utilizing AI in detection of voice disorders. Gaps included the use of limited, inconsistent data sets, lack of validation, and emphasis on detection rather than treatment of the voice disorder. These are areas of opportunity for AI techniques to improved diagnostic accuracy.
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