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Validation of an AI Model for Automated Detection of Alveolar Bone Changes Post-orthodontics Using Cone-Beam Computed
Shibam Bardhan1, Gaurav Nagar2, Kavya Adapala3
1Department of Orthodontics, Teerthanker Mahaveer Dental College and Research Centre, Moradabad, IND.
Introduction:
With recent advances in artificial intelligence (AI), image-based tools are increasingly being explored to assist clinicians in detecting changes in alveolar bone levels following orthodontic treatment. This retrospective, cross-sectional study aimed to develop and validate an automated AI model for quantifying alveolar bone levels using cone-beam computed tomography (CBCT) images from patients who underwent fixed orthodontic therapy, with the objective of curating a robust dataset, training AI models for landmark detection, evaluating accuracy against manual annotations, and assessing clinical applicability.
Materials And Methods:
At the Department of Orthodontics, 1,200 CBCT images from 200 patients (104 (52%) males, 96 (48%) females; mean age 23.6 ± 2.37 years) yielded 3,600 interdental sites. Images covering the maxillary and mandibular segments were preprocessed using median filtering and contrast-limited adaptive histogram equalization. The AI pipeline employed You Only Look Once, version 8 (YOLOv8; Ultralytics, Frederick, MD, USA) for tooth segmentation (720 training, 240 validation, and 240 test images) and a mask region-based convolutional neural network (Mask R-CNN) for generating tooth, bone, and crown masks. Custom algorithms were developed to localize the cementoenamel junction (CEJ) and alveolar bone crest (ALC). A random forest regression model was then applied to identify predictors of alveolar bone loss.
Results:
The cohort of 200 patients (104 males, 52%; 96 females, 48%; mean age 23.6 ± 2.37 years) showed alveolar bone loss in 124 patients (62%). The mean periodontal index was 1.90 ± 0.57 mm, and the mean orthodontic treatment duration was 26.72 ± 4.41 months. The mean ALC levels were 1.35 ± 0.65 mm in the anterior region and 1.47 ± 0.62 mm in the posterior region. YOLOv8 achieved a mean average precision (mAP) of 0.941 at an intersection-over-union (IoU) threshold of 0.5 (mAP50) and a tooth detection accuracy of 95.44% at complete IoU. Post-augmentation, Mask R-CNN yielded accuracies of 92.28% (tooth mask), 94.15% (bone mask), and 94.11% (crown mask). CEJ precision ranged from 85.3% to 94.5%, and ALC precision ranged from 82.8% to 95.3%, with a root mean square error of 0.023-0.071. Random forest regression identified treatment duration (importance score: 0.321) as the primary predictor of bone loss.
Conclusion:
This AI-driven pipeline offers an efficient and accurate tool for periodontal monitoring, enabling early detection of bone changes and supporting personalized post-orthodontic care. Future prospective studies with diverse cohorts are required to enhance clinical integration.
