Machine Learning useful to Diagnose Pediatric Obstructive Sl
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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.

A Study was conducted 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.

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) analysis were also conducted. Heterogeneity (I 2) was evaluated, and publication bias was corrected (trim and fill).

--19 studies were finally retained, involving 4,767 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.

To summarize, machine learning is capable of accurately detecting extreme OSA. However, further work is required to enhance diagnostic efficiency in children with less serious OSA and thus increase trust in these methods.