@文章{info:doi/ 10.296 /38472，作者=“Al-Hussain, Ghada和Shuweihdi, Farag和Alali, Haitham和Househ, Mowafa和Abd-alrazaq, Alaa”，标题=“监督机器学习在筛查和诊断语音障碍中的有效性:系统回顾和元分析”，期刊=“J医学互联网研究”，年=“2022”，月=“10”，日=“14”，卷=“24”，数=“10”，页=“e38472”，关键词=“机器学习;语音障碍;系统评价;荟萃分析;诊断;筛选;背景:在调查语音障碍时，包括语音筛查和诊断在内的一系列过程都被使用。这两种方法的标准化测试都有限，受临床医生经验和主观判断的影响。机器学习(ML)算法已被用作筛选或诊断语音障碍的客观工具。然而，ML算法在评估和诊断语音障碍方面的有效性还没有得到足够的学术重视。 Objective: This systematic review aimed to assess the effectiveness of ML algorithms in screening and diagnosing voice disorders. Methods: An electronic search was conducted in 5 databases. Studies that examined the performance (accuracy, sensitivity, and specificity) of any ML algorithm in detecting pathological voice samples were included. Two reviewers independently selected the studies, extracted data from the included studies, and assessed the risk of bias. The methodological quality of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool via RevMan 5 software (Cochrane Library). The characteristics of studies, population, and index tests were extracted, and meta-analyses were conducted to pool the accuracy, sensitivity, and specificity of ML techniques. The issue of heterogeneity was addressed by discussing possible sources and excluding studies when necessary. Results: Of the 1409 records retrieved, 13 studies and 4079 participants were included in this review. A total of 13 ML techniques were used in the included studies, with the most common technique being least squares support vector machine. The pooled accuracy, sensitivity, and specificity of ML techniques in screening voice disorders were 93{\%}, 96{\%}, and 93{\%}, respectively. Least squares support vector machine had the highest accuracy (99{\%}), while the K-nearest neighbor algorithm had the highest sensitivity (98{\%}) and specificity (98{\%}). Quadric discriminant analysis achieved the lowest accuracy (91{\%}), sensitivity (89{\%}), and specificity (89{\%}). Conclusions: ML showed promising findings in the screening of voice disorders. However, the findings were not conclusive in diagnosing voice disorders owing to the limited number of studies that used ML for diagnostic purposes; thus, more investigations are needed. While it might not be possible to use ML alone as a substitute for current diagnostic tools, it may be used as a decision support tool for clinicians to assess their patients, which could improve the management process for assessment. Trial Registration: PROSPERO CRD42020214438; https://www.crd.york.ac.uk/prospero/display{\_}record.php?RecordID=214438 ", issn="1438-8871", doi="10.2196/38472", url="//www.mybigtv.com/2022/10/e38472", url="https://doi.org/10.2196/38472", url="http://www.ncbi.nlm.nih.gov/pubmed/36239999" }