Optimizing CBCT analysis of the Adenoid region: A deep learning approach

Objectives: To develop a deep learning (DL) algorithm to segment the adenoid hypertrophy (AH) area from Cone Beam Computed Tomography (CBCT) scans to aid in the early detection of enlarged adenoids and improve management of AH. Methods: This retrospective study utilized CBCT scans, comprising oral radiologist-graded scans for training and validation, and a test dataset diagnosed by an Ear, Nose, and Throat (ENT) specialist using nasoendoscopy (NE), which served as the reference standard for external validation. Manual adenoid area segmentation was performed using 3D Slicer. A DL algorithm, based on convolutional neural networks, was developed to segment the naso- and oropharynx in CBCT images with and without AH. The Dice Similarity Coefficient and Intersection over Union were applied to assess segmentation accuracy. Results: A total of 96 CBCT scans, distributed by AH grading, reflected at least 22,800 DICOM manually segmented files. Evaluator calibration was confirmed within the intraclass correlation coefficient (ICC) 0.90. Data augmentation was applied, maintaining the dataset distribution. Four nnU-Net-based segmentation models were tested: 2D, 3D Full-resolution (3D Fullres), 3D Low-resolution (3D Lowres), and 3D Cascade. The algorithm achieved 0.90 overall accuracy, a 0.90 Dice score, and 0.08 precision on the test dataset for adenoid area segmentation. Conclusions: The trained nnU-Net model demonstrated excellent results in segmenting the AH region, (Dice score: 0.99) achieved for the combination of 3D Cascade and 3D Fullres models. When applied in available imaging, this DL integration with CBCT enhances early AH detection and streamlines referrals for timely treatment by medical teams.