Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta‐analysis
Gonzalo C. Gutiérrez‐Tobal, Daniel Álvarez, Leila Kheirandish‐Gozal, Félix del Campo, David Gozal, Roberto Hornero
Abstract
BACKGROUND: 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. OBJECTIVE: To assess the reliability of machine-learning-based methods to detect pediatric OSA. DATA SOURCES: Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references. ELIGIBILITY CRITERIA: 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. APPRAISAL AND SYNTHESIS METHODS: ) was evaluated, and publication bias was corrected (trim and fill). RESULTS: 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.