Reliability of Machine Learning to Diagnose Pediatric Obstructive Sleep Apnea: Systematic Review and Meta-Analysis

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 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 2) 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.