Objectives <p>To develop and validate the Q-Bone system, an intelligent quantitative system for anatomically driven assessment of alveolar bone loss and assistance in the diagnosis of periodontitis across multiple clinical centers and imaging devices.</p> Methods <p>This study included 1,273 periodontitis cases from four clinical centers using diverse imaging devices. A multitask deep learning model, Deep Gradient Network (DGNet), was employed for tooth segmentation and anatomical keypoint localization, and was integrated with an anatomically driven, curvature-based quantification algorithm for alveolar bone resorption ratio (ABRR) measurement. Performance was evaluated using internal and multicenter external datasets, including patient-level agreement analysis for Stage, Grade, and Extent.</p> Results <p>The Q-Bone system demonstrated strong performance: tooth segmentation achieved an S-measure of 0.929, and keypoint localization reached a PRCK@0.5 of 0.994 in internal validation. Tooth-level ABRR showed high agreement with specialist measurements, with an ICC of 0.973 and minimal bias (− 0.238%). In the multicenter clinical validation cohort (<i>n</i> = 174), agreement between Q-Bone and the specialist reference standard was high at the patient level, with Cohen’s κ values of 0.9351 for Stage, 0.9367 for Grade, and 0.9770 for Extent. For the ordinal outcomes of Stage and Grade, linear weighted κ values were 0.9508 and 0.9515, respectively.</p> Conclusions <p>The Q-Bone system enables automated tooth segmentation, anatomical keypoint localization, tooth-level quantification of alveolar bone loss, and patient-level assessment across Stage, Grade, and Extent. It showed high agreement with specialist reference standards across multicenter and cross-device settings, supporting its applicability as a standardized imaging-based assistance tool for periodontal evaluation.</p>