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The purpose of this study was to evaluate the feasibility and segmentation performance of an nnU-Net v2\u2013based deep learning model for the automatic segmentation and voxel-level localization of MRONJ lesions on cone-beam computed tomography (CBCT) images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, cone beam computed tomography images of 52 patients with MRONJ were used. All images were manually annotated by experienced oral and maxillofacial radiologists using 3D Slicer software to generate ground truth segmentations. The nnU-Net v2 3D low-resolution architecture, implemented within an automated training pipeline, was trained for 1000 epochs.<\/jats:p>\n                    <jats:p>Model evaluation was performed within the framework of the default five-fold cross-validation strategy of nnU-Net v2. Model performance was evaluated using accuracy, recall (sensitivity), precision, Dice coefficient (DC), Intersection over Union (IoU) and 95% Hausdorff distance (95% HD) metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The nnU-Net v2 algorithm generated predictions on the evaluated cases of MRONJ patients. The algorithm achieved average accuracy, recall, and precision scores of 0.999, 0.707, and 0.743, respectively. In addition, the DC, 95% HD, and IoU values were 0.716, 4.045 mm, and 0.569, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrates the feasibility of nnU-Net v2\u2013based automatic MRONJ segmentation on CBCT images. Although limited by dataset size, the results suggest potential clinical utility as a supportive tool rather than a standalone diagnostic system.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12903-026-08022-1","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T12:34:53Z","timestamp":1772714093000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning\u2013based automatic segmentation of MRONJ lesions on CBCT images"],"prefix":"10.1186","volume":"26","author":[{"given":"Alp","family":"P\u0131narba\u015f\u0131","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meryem","family":"Kayg\u0131s\u0131z Yi\u011fit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meryem","family":"Et\u00f6z","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"issue":"5","key":"8022_CR1","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1016\/j.joms.2022.02.008","volume":"80","author":"SL Ruggiero","year":"2022","unstructured":"Ruggiero SL, Dodson TB, Aghaloo T, Carlson ER, Ward BB, Kademani D. 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Ethical approval was obtained from the Erciyes University Health Sciences Research Ethics Committee (decision no: 2024\/299).Due to the retrospective nature of the study and the use of anonymized imaging data, the requirement for informed consent to participate was waived by the Erciyes University Health Sciences Research Ethics Committee in accordance with national regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"655"}}