Deep Learning based Quantification of Root Exposure in Lower Anterior Teeth using Intraoral camera image

This study compared and evaluated two widely used deep learning-based artificial intelligence (AI) models, U-Net++ and YOLOv11, for quantifying tooth and root exposure on intraoral camera images of the mandibular anterior lingual region. Intraoral images of the mandibular anterior lingual region were collected from 291 patients (mean age, 52.8 years) at a university hospital dental clinic with institutional review board approval (YUMC IRB 2021-07-019-002). A total of 266 eligible images (mean, 5.50 teeth per image; 3.70 teeth with root exposure) were annotated. YOLOv11 and U-Net++ were fine-tuned using five-fold cross-validation with data augmentation. Model performance was evaluated on a held-out test set of 40 images using Dice coefficient, Intersection over Union (IoU), accuracy, mean Average Precision at an IoU threshold of 0.5 (mAP50), Lins concordance correlation coefficient (CCC), and intraclass correlation coefficient (ICC). Confidence intervals were estimated using 10,000 bootstrap iterations. For tooth segmentation, U-Net++ demonstrated superior performance, with high accuracy (0.981), Dice coefficient (0.971), and IoU (0.944). In contrast, for root segmentation, YOLOv11 outperformed U-Net++, achieving higher Dice (0.860 vs. 0.746) and IoU (0.762 vs. 0.631). Notably, YOLOv11 showed stronger agreement with the ground truth for quantifying the exposed root ratio (ERR) (CCC, 0.973; ICC, 0.975). These findings suggest that accurate detection of root exposure is important for assessing periodontal tissue loss and that YOLOv11 is a promising model for root exposure quantification in intraoral images. YOLOv11-based quantification of root exposure may serve as a useful adjunctive AI tool for screening and monitoring periodontal conditions and may support individualized treatment planning.