Deep learning‐based identification of periodontal infrabony defects with regenerative potential: A multicenter retrospective study

Background Current radiographic evaluation of periodontal infrabony defects is subjective and prone to variability, limiting evidence‐based regenerative surgery planning. This study aimed to develop a dual‐model deep learning framework integrating YOLO V8 and nnU‐Net V2 to automate defect identification, quantification, and surgical guidance on parallel intraoral radiographs (PTRs). Methods A multicenter retrospective study utilized 580 PTRs from three institutions. The model combined YOLO V8 (tooth instance segmentation) and nnU‐Net V2 (tissue semantic segmentation) to localize anatomical landmarks, such as the cemento‐enamel junction (CEJ), lowest point of the defect (LP), alveolar crest (ALC), and root apex (APEX), and to calculate defect parameters (depth, width, angle). An internal dataset ( n = 387) derived from Shanghai Stomatological Hospital was used for model training and cross‐validation, while an external dataset ( n = 93) from two independent institutions (Shanghai Ninth People's Hospital and Tongji University) served for generalizability testing. The datasets were evaluated via 5‐fold cross‐validation. Performance metrics included Dice coefficient, mean absolute error (MAE), sensitivity, specificity, and F1‐score. Results The model achieved high precision in landmark localization (MAE: 0.20–0.54 mm) and strong generalizability, with external specificity of 0.96 and accuracy of 0.89. It outperformed specialists in diagnostic speed (1.5 vs. 2.1 min/patient) and accuracy (96% vs. 92%). Postoperative analysis demonstrated significant defect resolution. Defects were categorized via a color‐coded system (e.g., depth ≥3 mm recommended for regeneration), aligning with clinical guidelines. Conclusions This study pioneers a dual‐model AI framework for infrabony defect analysis, offering precise, guideline‐aligned surgical recommendations via a color‐coded system. By standardizing defect assessment and enhancing diagnostic efficiency, the model bridges radiographic interpretation and clinical decision‐making, advancing personalized periodontal care. Plain language summary A dual‐model AI framework combining YOLO V8 and nnU‐Net V2 accurately identifies and quantifies periodontal infrabony defects on radiographs, outperforming specialists in speed and diagnostic accuracy.