Photo-based deep learning for detection of pediatric adenoid hypertrophy.

Objective: This proof-of-concept study aimed to develop and preliminarily validate a hybrid artificial intelligence (AI) model that combines 2D facial photographs with clinical variables for non-invasive pediatric adenoid hypertrophy (AH) screening. Methods: 11,465 full-face photographs (2,097 children, 6-14 years) were retrospectively collected and labeled by adenoid-to-nasopharyngeal ratio (>0.6 was defined as AH). A multi-stage optimization framework was employed to develop a photo-based deep learning model (AHDNet). Subsequently, features from AHDNet were integrated with clinical variables to construct a hybrid model (HM). Comprehensive model evaluation was performed using multiple metrics and interpretability methods. AHDNet's performance was also benchmarked against 21 clinicians of varying experience. To support further model optimization, an open-access testing platform was provided. Results: For preliminary screening, AHDNet achieved an area under the receiver operating characteristic curve (AUC) of 0.669 (95% confidence interval [CI] 0.584-0.748), showing a discriminatory ability comparable to that of human raters. The HM exhibited improved performance with an AUC of 0.712 (95% CI 0.630-0.784) and more favorable calibration. Decision curve analysis (DCA) revealed that the HM maintained positive net benefit within the threshold range of 0.133 to 0.429. Interpretability analysis highlighted the eyes and mandibular angle as key discriminative regions and identified narrow maxillary dental arches and strained lip closure as significant clinical risk factors. Conclusions: As a proof-of-concept, the hybrid AI model may serve as a non-invasive tool to assist with preliminary screening for pediatric AH, potentially facilitating timely detection and reducing the risk of subsequent maxillofacial deformities associated with AH.