Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.
Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Zaragoza, Spain.
Pediatr Pulmonol. 2022 Aug;57(8):1931-1943. doi: 10.1002/ppul.25423. Epub 2021 Apr 30.
Machine-learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice.
To assess the reliability of machine-learning-based methods to detect pediatric OSA.
Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references.
Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard.
Pooled sensitivities and specificities were computed for three apnea-hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random-effect models were assumed. Summary receiver-operating characteristics (SROC) analyses were also conducted. Heterogeneity (I ) was evaluated, and publication bias was corrected (trim and fill).
Nineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.
机器学习方法在简化小儿阻塞性睡眠呼吸暂停（OSA）的诊断方面取得了有希望的结果。对这些研究进行全面的回顾和分析，可以提高从业者和医疗保健提供者在临床实践中实施这些方法的信心水平。
评估基于机器学习的方法检测小儿 OSA 的可靠性。
两名研究人员在 Web of Science 和 Scopus 上使用术语进行了电子搜索，并对研究及其参考文献进行了综述。
应用机器学习来检测小儿 OSA 的文章或综述（2000 年以后发表）；报告的数据包括能够得出真阳性、假阴性、真阴性和假阳性病例的信息；多导睡眠图作为诊断标准。
计算了三个呼吸暂停低通气指数（AHI）阈值（1 次/小时、5 次/小时和 10 次/小时）的合并敏感性和特异性。采用随机效应模型。还进行了综合接收者操作特征（SROC）分析。评估了异质性（I ），并进行了发表偏倚校正（修剪和填充）。
最终保留了 19 项研究，涉及 4767 项不同的儿科睡眠研究。随着 OSA 严重程度标准的增加，机器学习提高了诊断性能，达到了 AHI=10 e/h 的最佳值（敏感性为 0.652，特异性为 0.931，SROC 曲线下面积为 0.940）。发表偏倚校正对汇总统计数据的影响较小，但研究之间存在高度异质性。
