{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:31:33Z","timestamp":1787020293428,"version":"build-2736575974"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":24,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Oral Health"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial Intelligence (AI) is reshaping diagnostics and disease prevention in the dental domain. Panoramic X-ray imaging is central to this progress but demands large, high-quality annotated datasets. We therefore present AKUDENTAL, a new dataset for instance segmentation of dental radiographs, to serve as a resource for model development and to assess the challenges of generalizability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We annotated 333 panoramic images, labeling 9,956 structures across 32 individual teeth and three restorative categories: implants, bridges, and crown\u2013filling. We established semantic segmentation, object detection, and instance-segmentation baselines using UNet, DeepLabV3\u2009+\u2009, YOLOv11, and Mask R-CNN models. Generalizability was assessed via 5-fold cross-validation and a cross-dataset evaluation on the Tufts, DENTEX, and Dual-labeled datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A cross-dataset evaluation on the Tufts, DENTEX, and Dual-labeled datasets revealed that variations in annotation protocols are a significant factor contributing to performance differences. The cross-dataset evaluation demonstrated widely varying performance, with mean Average Precision (mAP) scores for multiclass detection ranging from a low of 0.34 on the DENTEX dataset to 0.71 on the Dual-labeled dataset Our analysis illustrates how such discrepancies can impact the interpretation of model performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The AKUDENTAL dataset provides a robust new resource for the field. The performance disparities revealed in our cross-dataset analysis are not model limitations but instead strengthen the argument that annotation inconsistencies are a critical barrier to developing universally applicable AI. This highlights the imperative for broader standardization in data annotation, extending beyond tooth identification to encompass common dental procedures and restorations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12903-025-07645-0","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T11:53:58Z","timestamp":1768218838000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AKUDENTAL teeth instance segmentation dataset: a cross-dataset analysis"],"prefix":"10.1186","volume":"26","author":[{"given":"Melih","family":"Oz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aycan","family":"Sengul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mukerrem","family":"Hatipoglu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taner","family":"Danisman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"issue":"4","key":"7645_CR1","doi-asserted-by":"publisher","first-page":"1650","DOI":"10.1109\/JBHI.2021.3117575","volume":"26","author":"K Panetta","year":"2021","unstructured":"Panetta K, Rajendran R, Ramesh A, Rao SP, Agaian S. Tufts dental database: a multimodal panoramic X-ray dataset for benchmarking diagnostic systems. IEEE J Biomed Health Inform. 2021;26(4):1650\u20139.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"7645_CR2","doi-asserted-by":"publisher","first-page":"8495","DOI":"10.1038\/s41598-019-44839-3","volume":"9","author":"J Krois","year":"2019","unstructured":"Krois J, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 2019;9(1):8495.","journal-title":"Sci Rep"},{"key":"7645_CR3","unstructured":"Hamamci IE, et al. DENTEX: an abnormal tooth detection with dental enumeration and diagnosis benchmark for panoramic X-rays. ArXiv Prepr. ArXiv230519112, 2023."},{"issue":"1","key":"7645_CR4","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1186\/s12903-024-04984-2","volume":"24","author":"W Zhou","year":"2024","unstructured":"Zhou W, et al. A dual-labeled dataset and fusion model for automatic teeth segmentation, numbering, and state assessment on panoramic radiographs. BMC Oral Health. 2024;24(1):1201.","journal-title":"BMC Oral Health"},{"key":"7645_CR5","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1038\/s41597-024-04306-9","volume":"12","author":"Y Wang","year":"2025","unstructured":"Wang Y, et al. A multi-modal dental dataset for semi-supervised deep learning image segmentation. Sci Data. 2025;12:117.","journal-title":"Sci Data"},{"issue":"4","key":"7645_CR6","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1080\/21681163.2022.2157747","volume":"11","author":"BPM Silva","year":"2023","unstructured":"Silva BPM, et al. Boosting research on dental panoramic radiographs: a challenging data set, baselines, and a task central online platform for benchmark. Comput Methods Biomech Biomed Eng. 2023;11(4):1327\u201347. https:\/\/doi.org\/10.1080\/21681163.2022.2157747.","journal-title":"Comput Methods Biomech Biomed Eng"},{"issue":"4","key":"7645_CR7","doi-asserted-by":"publisher","first-page":"20180051","DOI":"10.1259\/dmfr.20180051","volume":"48","author":"DV Tuzoff","year":"2019","unstructured":"Tuzoff DV, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019;48(4):20180051. https:\/\/doi.org\/10.1259\/dmfr.20180051.","journal-title":"Dentomaxillofac Radiol"},{"key":"7645_CR8","doi-asserted-by":"publisher","first-page":"2257","DOI":"10.1007\/s00784-020-03544-6","volume":"25","author":"AF Leite","year":"2021","unstructured":"Leite AF, et al. Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs. Clin Oral Investig. 2021;25:2257\u201367.","journal-title":"Clin Oral Investig"},{"issue":"1","key":"7645_CR9","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1186\/s12880-024-01338-w","volume":"24","author":"B Beser","year":"2024","unstructured":"Beser B, et al. YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition. BMC Med Imaging. 2024;24(1):172.","journal-title":"BMC Med Imaging"},{"key":"7645_CR10","doi-asserted-by":"publisher","unstructured":"Jader G, Fontineli J, Ruiz M, Abdalla K, Pithon M, Oliveira L. Deep instance segmentation of teeth in panoramic X-ray images. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). 2018. p. 400\u2013407. https:\/\/doi.org\/10.1109\/SIBGRAPI.2018.00058.","DOI":"10.1109\/SIBGRAPI.2018.00058"},{"key":"7645_CR11","doi-asserted-by":"publisher","unstructured":"Silva B, Pinheiro L, Oliveira L, Pithon M. A study on tooth segmentation and numbering using end-to-end deep neural networks. In: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). 2020. p. 164\u2013171. https:\/\/doi.org\/10.1109\/SIBGRAPI51738.2020.00030.","DOI":"10.1109\/SIBGRAPI51738.2020.00030"},{"issue":"8","key":"7645_CR12","doi-asserted-by":"publisher","first-page":"1635","DOI":"10.3390\/jcm10081635","volume":"10","author":"J Krois","year":"2021","unstructured":"Krois J, Schneider L, Schwendicke F. Impact of image context on deep learning for classification of teeth on radiographs. J Clin Med. 2021;10(8):1635.","journal-title":"J Clin Med"},{"key":"7645_CR13","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","volume":"77","author":"BC Russell","year":"2008","unstructured":"Russell BC, Torralba A, Murphy KP, Freeman WT. Labelme: a database and web-based tool for image annotation. Int J Comput Vis. 2008;77:157\u201373.","journal-title":"Int J Comput Vis"},{"key":"7645_CR14","unstructured":"Jocher G, Qiu J, Chaurasia A. Ultralytics YOLO. 2023. Available: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"7645_CR15","doi-asserted-by":"publisher","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R. Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV). 2017. p. 2980\u20132988. https:\/\/doi.org\/10.1109\/ICCV.2017.322.","DOI":"10.1109\/ICCV.2017.322"},{"key":"7645_CR16","doi-asserted-by":"crossref","unstructured":"Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV). 2018. p. 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"7645_CR17","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, proceedings, part III 18. Springer; 2015. p. 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28.","DOI":"10.1007\/978-3-319-24574-4_28"}],"container-title":["BMC Oral Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12903-025-07645-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-025-07645-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-025-07645-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T16:36:47Z","timestamp":1781627807000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12903-025-07645-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,12]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["7645"],"URL":"https:\/\/doi.org\/10.1186\/s12903-025-07645-0","relation":{},"ISSN":["1472-6831"],"issn-type":[{"value":"1472-6831","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,12]]},"assertion":[{"value":"7 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approval for this study was obtained from the Akdeniz University Clinical Research Ethical Committee (Approval No: KAEK-72 -Decision Date 2023\/01). All procedures followed were in accordance with the ethical standards of the committee responsible on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2013. The need for informed consent was waived by the Akdeniz University Clinical Research Ethical Committee due to the retrospective nature of the research and the use of fully anonymized data.","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":"247"}}