Deep learning-based automatic adenoid segmentation and a novel volume-based index for adenoid hypertrophy assessment
Xin Zhang1,2, Yuanyuan Li3,2, Xueying Wu4,2
1Department of Orthodontics, Shanghai Stomatological Hospital, School of Stomatology, Fudan University, Shanghai, China.
Background:
Adenoid hypertrophy is a common cause of pediatric obstructive sleep apnea (OSA), which can impair cognitive development and affect craniofacial development. Given that adenoids typically regress with age and may respond to oral appliance therapy, early identification, clear orthodontic indications, and timely referral are essential for effective management. A major challenge in this process is accurately assessing adenoid volume and quantifying nasopharyngeal obstruction in three dimensions. To address this, we developed a deep learning-based method using cone-beam computed tomography (CBCT) to accurately segment adenoid, and establish a novel quantitative index, the Three-Dimensional Adenoidal-Nasopharyngeal Ratio (3D-AN), for assessing nasopharyngeal airway obstruction.
Methods:
This retrospective study included three datasets. Dataset 1 comprised 126 CBCT scans (pre- and post-adenoidectomy) from 63 pediatric OSA patients. Dataset 2 included post-adenoidectomy CBCT scans of 26 pediatric OSA patients. Dataset 3 involved 161 pediatric patients with both polysomnography and CBCT data. A SegResNet-based model was trained on Datasets 1 and 2 (1326 training pairs) to predict postoperative airway morphology following adenoidectomy using preoperative scans. By inputting preoperative airway data, the model predicts postoperative morphology, and the volumetric difference represents the adenoid. 3D-AN ratio was then computed from adenoid-to-nasopharyngeal volume. Dataset 3 was used to investigate the relationship between the 3D-AN and pediatric OSA.
Results:
The segmentation model achieved a Dice similarity coefficient of 0.88 and a relative volume error of 0.09. Preliminary analysis showed that a 3D-AN ratio below 0.18 was not significantly correlated with pediatric OSA, while values above 0.18 correlated significantly with OSA (r = 0.56, P < 0.01).
Conclusions:
This automated method enables accurate adenoid segmentation. The proposed 3D-AN, based on volumetric ratios, holds potential for early detection of adenoid-related airway obstruction and for supporting future research in adenoid assessment and clinical management.
