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Artificial intelligence applications in CBCT-based assessment of craniofacial airway volume and shape in sleep-disordered breathing: A systematic review

dentistry
pathology
radiology-and-medical-imaging
human-computer-interaction
computational-science

Abstract

Background: Sleep-Disordered Breathing (SDB) refers to a spectrum of respiratory abnormalities that occur during sleep, ranging from benign snoring to severe conditions like obstructive sleep apnea (OSA). This systematic review evaluates the diagnostic performance and clinical utility of AI applications in CBCT-based assessment of craniofacial airway volume and shape in patients with Sleep-Disordered Breathing (SDB).
Methodology: A thorough search was conducted across four databases to identify studies published since 2015 that applied AI tools for CBCT airway analysis. Fourteen studies met the inclusion criteria and were assessed using the QUADAS-2 tool for risk of bias and applicability.
Results: Many studies used deep learning models, U-Net and SpatialConfiguration-Net, providing high segmentation accuracy with Dice Similarity Coefficients over 0.9. AI-based methods showed strong agreement with manual techniques, reduced analysis time, and consistent performance across anatomical variations. Some studies also demonstrated AI’s potential in predicting OSA severity.
Conclusion: AI-enhanced CBCT analysis is a reliable, quick, and reproducible to assess the upper airway structures in SDB patients.
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