Current role of artificial intelligence and machine learning: is their application feasible in pediatric upper airway obstructive disorders?

PURPOSE: The aim of this article was to conduct a systematic review to evaluate the role and reliability of artificial intelligence (AI) and machine learning (ML) in the diagnosis, management, and potential treatment of pediatric upper airway obstruction (UAO).

METHODS

This PRISMA-based review searched PubMed, Scopus, and Web of Science for English-language studies on pediatric UAO (≤ 18 years) using AI/ML. Non-original works, unrelated topics, mixed-age studies, and those without AI/ML were excluded.

RESULTS

Out of 76 identified articles, 27 were included in the review. Most studies on AI and ML focused on pediatric obstructive sleep apnea (OSA), particularly diagnosis and severity classification.Convolutional Neural Networks (CNNs) were the most common approach, used in 29% of studies. The most frequent input modality was nocturnal blood oxygen saturation (SpO₂) signals (44%), followed by clinical parameters (14.8%), electrocardiography (ECG) (7.4%), and polysomnography (PSG) data (7.4%). Model performance varied based on input data and study design. Advanced methods for OSA show high accuracy: deep learning (88.8%), actigraphy/oximetry (96%), and smartphone oximeters (> 79%). The Sunrise algorithm reached 100% sensitivity for severe OSA. Limitations across current studies include heterogeneous patient populations, small sample sizes, and a predominant focus on obstructive sleep apnea (OSA), which may restrict the generalizability of the findings.

CONCLUSIONS

In pediatric sleep medicine, ML models have focused on diagnosis mainly using physiological signalsand XGBoost/Support Vector Machines (SVM) for clinical data. No studies addressed treatment or monitoring, and challenges like data diversity, validation, and feasibility remain.