Deep Learning-Based Quantification of Adenoid Hypertrophy and Its Correlation with Apnea-Hypopnea Index in Pediatric Obstructive Sleep Apnea

Jie Cai,1,* Tianyu Xiu,2,* Yuliang Song,1 Xuwei Fan,3 Jianghao Wu,1 Aikebaier Tuohuti,1 Yifan Hu,1 Xiong Chen1,4 1Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430000, People’s Republic of China; 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, People’s Republic of China; 3School of Informatics, Xiamen University, Xiamen, 361000, People’s Republic of China; 4Sleep Medicine Center, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiong Chen; Tianyu Xiu, Email [email protected]; [email protected]: This study aims to develop a deep learning methodology for quantitative assessing adenoid hypertrophy in nasopharyngoscopy images and to investigate its correlation with the apnea-hypopnea index (AHI) in pediatric patients with obstructive sleep apnea (OSA).Patients and Methods: A total of 1642 nasopharyngoscopy images were collected from pediatric patients aged 3 to 12 years. After excluding images with obscured secretions, incomplete adenoid exposure, 1500 images were retained for analysis. The adenoid-to-nasopharyngeal (A/N) ratio was manually annotated by two experienced otolaryngologists using MATLAB’s imfreehand tool. Inter-annotator agreement was assessed using the Mann–Whitney U-test. Deep learning segmentation models were developed with the MMSegmentation framework, incorporating transfer learning and ensemble learning techniques. Model performance was evaluated using precision, recall, mean intersection over union (MIoU), overall accuracy, Cohen’s Kappa, confusion matrices, and receiver operating characteristic (ROC) curves. The correlation between the A/N ratio and AHI, derived from polysomnography, was analyzed to evaluate clinical relevance.Results: Manual evaluation of adenoid hypertrophy by otolaryngologists (p=0.8507) and MATLAB calibration (p=0.679) demonstrated high consistency, with no significant differences. Among the deep learning models, the ensemble learning-based SUMNet outperformed others, achieving the highest precision (0.9616), MIoU (0.8046), overall accuracy (0.9182), and Kappa (0.87). SUMNet also exhibited superior consistency in classifying adenoid sizes. ROC analysis revealed that SUMNet (AUC=0.85) outperformed expert evaluations (AUC=0.74). A strong positive correlation was observed between the A/N ratio and AHI, with the correlation coefficients for SUMNet-derived ratios ranging from r=0.9052 (tonsils size+1) to r=0.4452 (tonsils size+3) and for expert-derived ratios ranging from r=0.4590 (tonsils size+1) to r=0.2681 (tonsils size+3).Conclusion: This study introduces a precise and reliable deep learning-based method for quantifying adenoid hypertrophy and addresses the challenge posed limited sample sizes in deep learning applications. The significant correlation between adenoid hypertrophy and AHI underscores the clinical utility of this method in pediatric OSA diagnosis.Keywords: adenoid hypertrophy, obstructive sleep apnea (OSA), deep learning, transfer learning, ensemble learning