Investigating The Relationship Between Sleep Apnea And Upper Airway Malformations: Clinical And Radiological Insights: A Systematic Review And Meta-Analysis

Background: Sleep apnea, particularly obstructive sleep apnea (OSA), is a prevalent sleep disorder characterized by repeated upper airway obstruction, leading to fragmented sleep and intermittent hypoxia. Upper airway malformations, including craniofacial abnormalities and soft tissue hypertrophy, contribute significantly to OSA severity. Radiological imaging has emerged as a crucial tool in assessing airway morphology, but the exact relationship between anatomical variations and OSA remains unclear. This study aims to investigate the correlation between upper airway malformations and OSA severity through clinical and radiological insights. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines. Studies assessing the relationship between upper airway malformations and OSA severity using clinical and radiological imaging techniques (CT, MRI, CBCT) were included. Sleep apnea severity was measured using the Apnea-Hypopnea Index (AHI), oxygen desaturation index, and other relevant parameters. Data extraction and quality assessment were performed using validated tools, and statistical analysis was conducted to determine correlations between anatomical abnormalities and OSA severity. Results: The meta-analysis confirmed significant associations between airway structure variations and OSA severity. Studies demonstrated that reduced cross-sectional airway area (CSA), increased tonsil volume, and greater neck circumference were reliable predictors of severe OSA. Patients with craniofacial abnormalities, such as retrognathia and maxillary constriction, exhibited higher AHI scores. Radiological imaging techniques, particularly CBCT, provided enhanced anatomical insights, though variability in study methodologies limited comparability. Artificial intelligence (AI)-based algorithms showed potential in improving the accuracy of radiological assessments. Conclusion: The findings highlight the multifactorial nature of OSA, with structural, physiological, and neuromechanical factors contributing to its severity. Radiological imaging plays a pivotal role in diagnosis and treatment planning, yet standardized assessment protocols are needed for better clinical applicability. Advancements in AI and machine learning may further enhance diagnostic precision. Future research should focus on integrating imaging, clinical evaluation, and functional assessments to optimize OSA management.