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Published on: February 23, 2024
Artificial intelligence for upper-airway segmentation, adenoid-hypertrophy assessment, and sinonasal image analysis
Hugo Henrique Dos Santos Dantas Guimarães1, Karla Nogueira Matos2
1Department of Restorative Dentistry, Federal University of Minas Gerais, Minas Gerais, Brazil.
Objectives:
To systematically evaluate evidence on artificial intelligence (AI) for upper airway-related image analysis, with emphasis on upper-airway segmentation, adenoid-hypertrophy assessment, and sinonasal applications in orthodontic and craniofacial imaging.
Material And Methods:
The protocol was registered in PROSPERO (CRD420261405535). Eligible studies assessed AI-based segmentation, detection, classification, volumetric assessment, software validation, or landmark localization using CBCT, CT, lateral cephalograms, panoramic radiographs, or CBCT-derived images. Data were synthesized qualitatively. The primary meta-analysis included studies reporting Dice similarity coefficient as mean and standard deviation for AI-based three-dimensional CBCT/CT upper or pharyngeal-airway segmentation, and a sensitivity analysis was performed in a more methodologically comparable subset. Risk of bias was assessed with QUADAS-2 and certainty of evidence with GRADE.
Results:
Twenty-four studies were included across six domains. Seven studies were included in the primary meta-analysis, yielding a pooled Dice similarity coefficient of 0.935 (95% CI, 0.918-0.951; I2=48.6%). Sensitivity analysis restricted to four studies yielded a pooled Dice similarity coefficient of 0.956 (95% CI, 0.940-0.971; I2=28.5%). AI performance was generally favourable but varied according to imaging modality, anatomical target, model architecture, reference standard, population characteristics, and validation strategy. Evidence for adenoid-hypertrophy assessment was expanded by the inclusion of an additional CBCT-based nnU-Net study. External and multicentre validation remained limited, and certainty of evidence was moderate across domains.
Conclusions:
AI-based methods show promising performance, particularly for three-dimensional airway segmentation and emerging adenoid-hypertrophy applications. However, anatomical and methodological heterogeneity limits direct cross-study comparisons. Standardized anatomical definitions, representative populations, external validation, and clinically meaningful reference standards are needed before broad implementation.
