Deep learning-based automatic adenoid segmentation and a novel volume-based index for adenoid hypertrophy assessment.

ABSTRACT:

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 obstructio

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12930684 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature