TY -的盟Al-Hussain Ghada盟——Shuweihdi法拉克盟——Alali Haitham AU - Househ, Mowafa盟——Abd-alrazaq Alaa PY - 2022 DA - 2022/10/14 TI -监督机器学习的有效性在筛查和诊断声音障碍:系统回顾和荟萃分析乔- J地中海互联网Res SP - e38472六世- 24 - 10 KW——机器学习KW -语音障碍千瓦系统回顾KW -荟萃分析千瓦诊断KW -筛选KW -手机AB -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。目的:本系统综述旨在评估ML算法在语音障碍筛查和诊断中的有效性。方法:在5个数据库中进行电子检索。研究检查了任何ML算法在检测病理声音样本中的性能(准确性、敏感性和特异性)。两名审稿人独立选择了这些研究，从纳入的研究中提取了数据，并评估了偏倚的风险。使用RevMan 5软件(Cochrane图书馆)的诊断准确性研究质量评估2工具评估各研究的方法学质量。 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 SN - 1438-8871 UR - //www.mybigtv.com/2022/10/e38472 UR - https://doi.org/10.2196/38472 UR - http://www.ncbi.nlm.nih.gov/pubmed/36239999 DO - 10.2196/38472 ID - info:doi/10.2196/38472 ER -