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Current role of artificial intelligence and machine learning: is their application feasible in pediatric upper airway
Virginia Dallari1,2,3, Marella Reale4,5, Matteo Fermi2,6,7
1Department of Otolaryngology Head and Neck Surgery, Santa Maria delle Croci Hospital, AUSL della Romagna, Ravenna, Italy.
Insights
Artificial intelligence and machine learning show promise in diagnosing pediatric obstructive sleep apnea using physiological signals. However, current research lacks treatment applications and faces challenges in data diversity and validation.
Area of Science:
- Pediatric Sleep Medicine
- Medical Artificial Intelligence
- Machine Learning in Healthcare
Background:
- Pediatric upper airway obstruction (UAO) is a significant clinical concern.
- Artificial intelligence (AI) and machine learning (ML) offer potential advancements in medical diagnostics and management.
Purpose of the Study:
- To systematically review the role and reliability of AI and ML in the diagnosis, management, and treatment of pediatric UAO.
- To evaluate current AI/ML applications in pediatric sleep medicine.
Main Methods:
- A PRISMA-based systematic review of English-language studies.
- Searched PubMed, Scopus, and Web of Science for studies on pediatric UAO (≤18 years) using AI/ML.
- Excluded non-original works, unrelated topics, mixed-age studies, and those without AI/ML.
Main Results:
- 27 of 76 identified articles were included, primarily focusing on pediatric obstructive sleep apnea (OSA) diagnosis and severity.
- Convolutional Neural Networks (CNNs) were common (29%), often using nocturnal SpO₂ signals (44%).
- High accuracy reported for advanced methods like deep learning (88.8%) and actigraphy/oximetry (96%); Sunrise algorithm showed 100% sensitivity for severe OSA.
Conclusions:
- ML models in pediatric sleep medicine predominantly focus on diagnosis using physiological signals and clinical data.
- No studies addressed AI/ML for treatment or monitoring of pediatric UAO.
- Challenges include data diversity, validation, and feasibility for broader clinical implementation.
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.
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