An AI-Based Framework for Automated Radiographic Bone Loss Measurement Using Segmentation and Geometric Landmark Modeling

Accurate assessment of radiographic bone loss (RBL) is essential for periodontal diagnosis and staging; however, manual measurement from dental radiographs is labor-intensive, time-consuming and subject to inter- and intra-examiner variability. Existing AI-based methods primarily formulate bone loss assessment as classification, landmark prediction, or direct segmentation of thin anatomical structures, limiting measurement interpretability and robustness. This study proposes clinically interpretable two-phase framework for automated and clinically interpretable RBL estimation from periapical radiographs. The framework explicitly separates anatomical structure recognition from geometric measurement, improving transparency and reducing error propagation. In the first phase, deep learning models segment key anatomical structures, including the crown, root, third root and alveolar bone. In the second phase, a deterministic geometric algorithm extracts clinically relevant landmarks, including the cemento–enamel junction (CEJ), bone crest, and root apex, and computes root length, CEJ–bone crest distance, and radiographic bone loss following established periodontal measurement principles. The framework was evaluated on a curated dataset of annotated radiographs. DS-TransUNet achieved the best segmentation performance. Quantitative evaluation yielded mean absolute errors of 0.81 mm for CEJ–bone crest distance, 0.71 mm for root length, and 5.89% for RBL estimation. Bland–Altman analysis demonstrated minimal systematic bias (−1.03%) and good agreement with expert measurements across different disease severities, supporting the framework’s potential as an objective and clinically applicable tool for periodontal bone loss assessment.