{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T22:13:03Z","timestamp":1790115183628,"version":"4.0.1"},"reference-count":30,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aap.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Periodontology"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Current radiographic evaluation of periodontal infrabony defects is subjective and prone to variability, limiting evidence\u2010based regenerative surgery planning. This study aimed to develop a dual\u2010model deep learning framework integrating YOLO V8 and nnU\u2010Net V2 to automate defect identification, quantification, and surgical guidance on parallel intraoral radiographs (PTRs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A multicenter retrospective study utilized 580 PTRs from three institutions. The model combined YOLO V8 (tooth instance segmentation) and nnU\u2010Net V2 (tissue semantic segmentation) to localize anatomical landmarks, such as the cemento\u2010enamel 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                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0387) derived from Shanghai Stomatological Hospital was used for model training and cross\u2010validation, while an external dataset (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a093) from two independent institutions (Shanghai Ninth People's Hospital and Tongji University) served for generalizability testing. The datasets were evaluated via 5\u2010fold cross\u2010validation. Performance metrics included Dice coefficient, mean absolute error (MAE), sensitivity, specificity, and F1\u2010score.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved high precision in landmark localization (MAE: 0.20\u20130.54\u00a0mm) and strong generalizability, with external specificity of 0.96 and accuracy of 0.89. It outperformed specialists in diagnostic speed (1.5\u00a0vs. 2.1 min\/patient) and accuracy (96%\u00a0vs. 92%). Postoperative analysis demonstrated significant defect resolution. Defects were categorized via a color\u2010coded system (e.g., depth \u22653\u00a0mm recommended for regeneration), aligning with clinical guidelines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study pioneers a dual\u2010model AI framework for infrabony defect analysis, offering precise, guideline\u2010aligned surgical recommendations via a color\u2010coded system. By standardizing defect assessment and enhancing diagnostic efficiency, the model bridges radiographic interpretation and clinical decision\u2010making, advancing personalized periodontal care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Plain language summary<\/jats:title>\n                    <jats:p>A dual\u2010model AI framework combining YOLO V8 and nnU\u2010Net V2 accurately identifies and quantifies periodontal infrabony defects on radiographs, outperforming specialists in speed and diagnostic accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jper.70039","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T04:51:47Z","timestamp":1765428707000},"page":"807-819","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning\u2010based identification of periodontal infrabony defects with regenerative potential: A multicenter retrospective study"],"prefix":"10.1002","volume":"97","author":[{"given":"Yiru","family":"Xia","sequence":"first","affiliation":[{"name":"Department of Periodontology Shanghai Stomatological Hospital &amp; 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School of Stomatology Fudan University  Shanghai China"},{"name":"Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases Fudan University  Shanghai China"},{"name":"Department of Periodontology Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine College of Stomatology Shanghai Jiao Tong University National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Shu","sequence":"additional","affiliation":[{"name":"Department of Periodontology Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine College of Stomatology Shanghai Jiao Tong University National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Periodontology Stomatological Hospital and Dental School of Tongji University Shanghai Engineering Research Center of Tooth Restoration and Regeneration  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dahong","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Shanghai Jiao Tong University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufeng","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Periodontology Shanghai Stomatological Hospital &amp; 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