ArticleCureus2025
Validation of an AI Model for Automated Detection of Alveolar Bone Changes Post-orthodontics Using Cone-Beam Computed Tomography.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Comparative diagnostic accuracy of a You Only Look Once-11s deep learning model and human observers for detection of periapical lesions on intraoral periapical radiographs.Journal of conservative dentistry and endodontics · 2026Article
- Current Applications and Future Perspectives of Artificial Intelligence in Face-Driven Orthodontics: A Scoping Review.Biomimetics (Basel, Switzerland) · 2026Review
- Machine learning for classification of periodontal defects (vertical versus horizontal) using CBCT datasets.Bioinformation · 2026Article
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7 authors.
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Abstract
introductionWith 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
methodsAt 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.
resultsThe 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.
conclusionThis 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.
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