Development of a Personalized Mandibular Bone Health Score using Explainable Artificial Intelligence and Cone-beam Computed Tomography Radiomics for Opportunistic Osteoporosis Screening

Abstract

Background Osteoporosis is a systemic skeletal disorder characterized by reduced bone strength and increased susceptibility to fragility fracture. Although dual-energy X-ray absorptiometry (DXA) remains the principal method for assessing bone mineral density (BMD), a substantial proportion of individuals at risk are not identified before clinical presentation. Dental cone-beam computed tomography (CBCT) examinations are routinely acquired for several dentomaxillofacial indications and contain three-dimensional information about mandibular cortical and trabecular architecture. Such examinations may therefore provide an opportunity for opportunistic assessment of skeletal health without additional radiation exposure. Objective To develop a personalized Mandibular Bone Health Score (MBHS) using conventional mandibular measurements, CBCT-derived radiomic features and selected clinical variables within an explainable artificial intelligence framework for opportunistic identification of individuals at increased probability of low BMD. Methods This study proposes a retrospective diagnostic prediction-model design involving adults who have undergone clinically indicated mandibular CBCT and DXA examination within a predefined temporal interval. A standardized mandibular region of interest will be segmented from CBCT volumes. Conventional imaging variables, including mandibular cortical measurements and morphological characteristics, will be combined with standardized radiomic descriptors of mandibular bone architecture. Prespecified clinical variables will also be incorporated. Radiomic features will undergo reproducibility assessment, preprocessing, dimensionality reduction and nested feature selection. Clinical-only, conventional-imaging, radiomics-only and integrated models will be developed and compared. Nested cross-validation will be used to reduce optimism and prevent information leakage. The final model will estimate the probability of low BMD and transform this probability into a continuous MBHS ranging from 0 to 100. SHapley Additive exPlanations (SHAP) will be used to provide global and individual-level explanations. Expected outcome The proposed framework is expected to determine whether quantitative mandibular CBCT information provides incremental predictive value beyond conventional mandibular measurements and clinical variables. Model performance will be evaluated using discrimination, calibration and decision-curve analysis. Conclusion The MBHS represents a proposed explainable, opportunistic screening framework that could transform routinely acquired dental CBCT examinations into an additional source of information regarding systemic skeletal health. It is intended to identify patients who may benefit from formal osteoporosis assessment and is not intended to replace DXA. Independent external validation across populations, institutions and CBCT systems will be essential before clinical implementation.