{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T03:09:48Z","timestamp":1774494588154,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Deep learning tools based on computer vision have emerged as alternative methods for assessing radiographic image patterns. These approaches have been explored for various forensic applications, including sex and age estimation. This study aimed to evaluate the diagnostic accuracy of a Convolutional Neural Network (CNN) in classifying radiographic images by sex and age, focusing on the nasal aperture as the morphological feature of interest. The sample comprised 9,349 radiographs annotated for the nasal aperture region. A CNN architecture based on the You Only Look Once series\u2014specifically the intermediate version 11 for object classification (YOLO11m-cls)\u2014was implemented, with training performed using 5-fold cross-validation. The overall accuracy rate was 74% (ranging from 61% to 88%), and the area under the Receiver Operating Characteristic (ROC) curve was 0.74. Correct classification rates were 73% for males and 75.17% for females. Accuracy varied with age, showing a 10% decrease among younger individuals compared to older ones. The study confirmed the reduced expression of sexually dimorphic traits in younger individuals and supported existing recommendations against performing sex estimation in subadults. Within the present methodological framework, the nasal aperture demonstrated limited applicability for sex estimation in the studied sample, with an accuracy rate corresponding to approximately one misclassification out of every four predictions.<\/jats:p>","DOI":"10.1038\/s41598-025-24593-5","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T12:17:08Z","timestamp":1763554628000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["YOLO11m-cls applied to sex and age classification based on the radiographic analysis of the nasal aperture"],"prefix":"10.1038","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1491-3435","authenticated-orcid":false,"given":"Leonardo","family":"Scavassini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4816-7528","authenticated-orcid":false,"given":"Rianne","family":"Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3618-450X","authenticated-orcid":false,"given":"Amber","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5502-2747","authenticated-orcid":false,"given":"Wahaj","family":"Anees","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8806-2442","authenticated-orcid":false,"given":"Jared","family":"Murray","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8511-4645","authenticated-orcid":false,"given":"Nikolaos","family":"Angelakopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5077-5136","authenticated-orcid":false,"given":"Marianna","family":"Arakelyan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2170-427X","authenticated-orcid":false,"given":"Lucas","family":"Porto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9771-9123","authenticated-orcid":false,"given":"Andr\u00e9","family":"Abade","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1417-2781","authenticated-orcid":false,"given":"Ademir","family":"Franco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"24593_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.archoralbio.2017.12.001","volume":"87","author":"J Adserias-Garriga","year":"2018","unstructured":"Adserias-Garriga, J., Thomas, C., Ubelaker, D. H. & Zapico, S. C. When forensic odontology Met biochemistry: multidisciplinary approach in forensic human identification. Arch. Oral Biol. 87, 7\u201314. https:\/\/doi.org\/10.1016\/j.archoralbio.2017.12.001 (2018).","journal-title":"Arch. Oral Biol."},{"issue":"sup1","key":"24593_CR2","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1080\/07853890.2021.1893522","volume":"53","author":"E Cunha","year":"2021","unstructured":"Cunha, E. Aging the death: the importance of having better methods for age at death Estimation of old individuals. Ann. Med. 53 (sup1), S1. https:\/\/doi.org\/10.1080\/07853890.2021.1893522 (2021).","journal-title":"Ann. Med."},{"issue":"11","key":"24593_CR3","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.3390\/healthcare9111545","volume":"9","author":"A Thurzo","year":"2021","unstructured":"Thurzo, A. et al. Use of advanced artificial intelligence in forensic medicine, forensic anthropology and clinical anatomy. Healthc. (Basel). 9 (11), 1545. https:\/\/doi.org\/10.3390\/healthcare9111545 (2021).","journal-title":"Healthc. (Basel)"},{"key":"24593_CR4","doi-asserted-by":"publisher","first-page":"100319","DOI":"10.1016\/j.fsisyn.2023.100319","volume":"6","author":"M Lo","year":"2023","unstructured":"Lo, M., Mariconti, E., Nakhaeizadeh, S. & Morgan, R. M. Preparing computed tomography images for machine learning in forensic and virtual anthropology. Forensic Sci. Int. Synergy. 6, 100319. https:\/\/doi.org\/10.1016\/j.fsisyn.2023.100319 (2023).","journal-title":"Forensic Sci. Int. Synergy"},{"issue":"1","key":"24593_CR5","doi-asserted-by":"publisher","first-page":"30136","DOI":"10.1038\/s41598-024-81718-y","volume":"14","author":"R Lye","year":"2024","unstructured":"Lye, R. et al. Deep learning versus human assessors: forensic sex Estimation from three-dimensional computed tomography scans. Sci. Rep. 14 (1), 30136. https:\/\/doi.org\/10.1038\/s41598-024-81718-y (2024).","journal-title":"Sci. Rep."},{"issue":"1","key":"24593_CR6","doi-asserted-by":"publisher","first-page":"17279","DOI":"10.1038\/s41598-022-21294-1","volume":"12","author":"A Franco","year":"2022","unstructured":"Franco, A. et al. Diagnostic performance of convolutional neural networks for dental sexual dimorphism. Sci. Rep. 12 (1), 17279. https:\/\/doi.org\/10.1038\/s41598-022-21294-1 (2022).","journal-title":"Sci. Rep."},{"issue":"1","key":"24593_CR7","doi-asserted-by":"publisher","first-page":"4668","DOI":"10.1038\/s41598-024-55497-5","volume":"14","author":"A Franco","year":"2024","unstructured":"Franco, A. et al. Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years. Sci. Rep. 14 (1), 4668. https:\/\/doi.org\/10.1038\/s41598-024-55497-5 (2024).","journal-title":"Sci. Rep."},{"issue":"3","key":"24593_CR8","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1080\/20961790.2021.2008705","volume":"7","author":"M Zhang","year":"2022","unstructured":"Zhang, M. Forensic imaging: a powerful tool in modern forensic investigation. Forensic Sci. Res. 7 (3), 385\u2013392. https:\/\/doi.org\/10.1080\/20961790.2021.2008705 (2022).","journal-title":"Forensic Sci. Res."},{"key":"24593_CR9","doi-asserted-by":"publisher","unstructured":"Ghosh, A., Sufian, A., Sultana, F., Chakrabarti, A. & De, D. Fundamental concepts of convolutional neural network. In: Balas VE, Kumar R, Srivastava R. Recent trends and advances in artificial intelligence and internet of things. Intelligent Systems Reference Library. Switzerland: Springer; (2020). https:\/\/doi.org\/10.1007\/978-3-030-32644-9 pp. 519\u2013567.","DOI":"10.1007\/978-3-030-32644-9"},{"issue":"360","key":"24593_CR10","doi-asserted-by":"publisher","first-page":"100723","DOI":"10.1016\/j.morpho.2023.100723","volume":"108","author":"J Murray","year":"2024","unstructured":"Murray, J. et al. Applying artificial intelligence to determination of legal age of majority from radiographic data. Morphologie 108 (360), 100723. https:\/\/doi.org\/10.1016\/j.morpho.2023.100723 (2024).","journal-title":"Morphologie"},{"key":"24593_CR11","doi-asserted-by":"publisher","first-page":"100332","DOI":"10.1016\/j.fsir.2023.100332","volume":"8","author":"P Nonthasaen","year":"2023","unstructured":"Nonthasaen, P., Mahikul, W., Chobpenthai, T. & Achararit, P. Sex Estimation from Thai hand radiographs using convolutional neural networks. Forensic Sci. Int. Rep. 8, 100332. https:\/\/doi.org\/10.1016\/j.fsir.2023.100332 (2023).","journal-title":"Forensic Sci. Int. Rep."},{"issue":"3","key":"24593_CR12","doi-asserted-by":"publisher","first-page":"239","DOI":"10.5624\/isd.20220016","volume":"52","author":"M Khazaei","year":"2022","unstructured":"Khazaei, M., Mollabashi, V., Khotanlou, H. & Farhadian, M. Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network. Imaging Sci. Dent. 52 (3), 239\u2013244. https:\/\/doi.org\/10.5624\/isd.20220016 (2022).","journal-title":"Imaging Sci. Dent."},{"key":"24593_CR13","doi-asserted-by":"publisher","first-page":"111704","DOI":"10.1016\/j.forsciint.2023.111704","volume":"348","author":"W Bu","year":"2023","unstructured":"Bu, W. et al. Automatic sex Estimation using deep convolutional neural network based on orthopantomogram images. Forensic Sci. Int. 348, 111704. https:\/\/doi.org\/10.1016\/j.forsciint.2023.111704 (2023).","journal-title":"Forensic Sci. Int."},{"issue":"362","key":"24593_CR14","doi-asserted-by":"publisher","first-page":"100772","DOI":"10.1016\/j.morpho.2024.100772","volume":"108","author":"A Franco","year":"2024","unstructured":"Franco, A. et al. Radiographic morphology of canines tested for sexual dimorphism via convolutional-neural-network-based artificial intelligence. Morphologie 108 (362), 100772. https:\/\/doi.org\/10.1016\/j.morpho.2024.100772 (2024).","journal-title":"Morphologie"},{"issue":"1","key":"24593_CR15","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1186\/s12880-025-01809-8","volume":"25","author":"HE Ulubaba","year":"2025","unstructured":"Ulubaba, H. E., Atik, \u0130., \u00c7ift\u00e7i, R., Eken, \u00d6. & Aldhahi, M. I. Deep learning for gender Estimation using hand radiographs: a comparative evaluation of CNN models. BMC Med. Imaging. 25 (1), 260. https:\/\/doi.org\/10.1186\/s12880-025-01809-8 (2025).","journal-title":"BMC Med. Imaging"},{"issue":"3","key":"24593_CR16","doi-asserted-by":"publisher","first-page":"400","DOI":"10.23907\/2016.041","volume":"6","author":"A Plemons","year":"2016","unstructured":"Plemons, A. & Hefner, J. T. Ancestry Estimation using macromorphoscopic traits. Acad. Forensic Pathol. 6 (3), 400\u2013412. https:\/\/doi.org\/10.23907\/2016.041 (2016).","journal-title":"Acad. Forensic Pathol."},{"key":"24593_CR17","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1111\/1556-4029.12421","volume":"59","author":"JT Hefner","year":"2014","unstructured":"Hefner, J. T. & Ousley, S. D. Statistical classification methods for estimating ancestry using morphoscopic traits. J. Forensic Sci. 59, 883\u2013890. https:\/\/doi.org\/10.1111\/1556-4029.12421 (2014).","journal-title":"J. Forensic Sci."},{"key":"24593_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s00414-025-03529-8","author":"T Mbonani","year":"2025","unstructured":"Mbonani, T., L\u2019Abb\u00e9, E., Chen, D. G. & Ridel, A. Population affinity Estimation in forensic anthropology: a South African perspective. Int. J. Legal Med. https:\/\/doi.org\/10.1007\/s00414-025-03529-8 (2025).","journal-title":"Int. J. Legal Med."},{"issue":"5","key":"24593_CR19","doi-asserted-by":"publisher","first-page":"e0284219","DOI":"10.1371\/journal.pone.0284219","volume":"18","author":"R Scendoni","year":"2023","unstructured":"Scendoni, R. et al. Anthropometric analysis of orbital and nasal parameters for sexual dimorphism: new anatomical evidences in the field of personal identification through a retrospective observational study. PLoS One. 18 (5), e0284219. https:\/\/doi.org\/10.1371\/journal.pone.0284219 (2023).","journal-title":"PLoS One"},{"issue":"2","key":"24593_CR20","doi-asserted-by":"publisher","first-page":"327","DOI":"10.4067\/s0717-95022009000200007","volume":"27","author":"MC L\u00f3pez","year":"2009","unstructured":"L\u00f3pez, M. C., Galdames, I. C. S., Matamala, D. A. Z. & Smith, R. L. Sexual dimorphism determination by piriform aperture morphometric analysis in Brazilian human skulls. Int. J. Morphol. 27 (2), 327\u2013331. https:\/\/doi.org\/10.4067\/s0717-95022009000200007 (2009).","journal-title":"Int. J. Morphol."},{"key":"24593_CR21","doi-asserted-by":"publisher","unstructured":"McDowell, J. L., L\u2019Abb\u00e9, E. N. & Kenyhercz, M. W. Nasal aperture shape evaluation between black and white South Africans. Forensic Sci. Int. 222 (1\u20133). https:\/\/doi.org\/10.1016\/j.forsciint.2012.06.007 (2012). 397.e1\u20136.","DOI":"10.1016\/j.forsciint.2012.06.007"},{"issue":"3","key":"24593_CR22","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1148\/radiol.2015151516","volume":"277","author":"PM Bossuyt","year":"2015","unstructured":"Bossuyt, P. M. et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology 277 (3), 826\u2013832. https:\/\/doi.org\/10.1148\/radiol.2015151516 (2015).","journal-title":"Radiology"},{"key":"24593_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2025.105867","author":"SE Uribe","year":"2025","unstructured":"Uribe, S. E. et al. Evaluating dental AI research papers: key considerations for editors and reviewers. J. Dent. https:\/\/doi.org\/10.1016\/j.jdent.2025.105867 (2025).","journal-title":"J. Dent."},{"key":"24593_CR24","unstructured":"Jocher, G. & Qiu, J. Ultralytics YOLO11 [software]. Version 11.0.0. (2024). Available from: https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"24593_CR25","doi-asserted-by":"publisher","unstructured":"Wang, C-Y., Bochkovskiy, A. & Liao, H-Y-M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023; Vancouver, Canada. pp. 7464\u201375. https:\/\/doi.org\/10.1109\/CVPR52729.2023.00721","DOI":"10.1109\/CVPR52729.2023.00721"},{"issue":"9","key":"24593_CR26","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3390\/math13091419","volume":"13","author":"M Tariq","year":"2025","unstructured":"Tariq, M. & Choi, K. YOLO11-driven deep learning approach for enhanced detection and visualization of wrist fractures in X-ray images. Mathematics 13 (9), 1419. https:\/\/doi.org\/10.3390\/math13091419 (2025).","journal-title":"Mathematics"},{"key":"24593_CR27","unstructured":"Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI); pp. 1137\u201345. (1995)."},{"key":"24593_CR28","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data mining, Inference and Prediction (Springer, 2009).","DOI":"10.1007\/978-0-387-84858-7"},{"issue":"1","key":"24593_CR29","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1097\/00000637-199807000-00012","volume":"41","author":"M Akg\u00fcner","year":"1998","unstructured":"Akg\u00fcner, M., Barut\u00e7u, A. & Karaca, C. Adolescent growth patterns of the bony and cartilaginous framework of the nose: a cephalometric study. Ann. Plast. Surg. 41 (1), 66\u201369. https:\/\/doi.org\/10.1097\/00000637-199807000-00012 (1998).","journal-title":"Ann. Plast. Surg."},{"key":"24593_CR30","unstructured":"Abadi, M. et al. TensorFlow: Large-scale machine learning on heterogeneous systems; 2015. Software available from https:\/\/tensorflow.org."},{"key":"24593_CR31","unstructured":"Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009)."},{"issue":"2","key":"24593_CR32","doi-asserted-by":"publisher","first-page":"78","DOI":"10.4103\/mj.mj_67_23","volume":"23","author":"BS Ominde","year":"2024","unstructured":"Ominde, B. S., Ikubor, J. E., Jaiyeoba-Ojigho, J. E., Omoro, O. F. & Igbigbi, P. S. Morphometric assessment of the piriform aperture and its clinical and forensic applications. Mustansiriya Med. J. 23 (2), 78\u201383. https:\/\/doi.org\/10.4103\/mj.mj_67_23 (2024).","journal-title":"Mustansiriya Med. J."},{"issue":"1","key":"24593_CR33","doi-asserted-by":"publisher","first-page":"AC05","DOI":"10.7860\/JCDR\/2016\/15677.7148","volume":"10","author":"A Asghar","year":"2016","unstructured":"Asghar, A., Dixit, A. & Rani, M. Morphometric study of nasal bone and piriform aperture in human dry skull of Indian origin. J. Clin. Diagn. Res. 10 (1), AC05\u20137. https:\/\/doi.org\/10.7860\/JCDR\/2016\/15677.7148 (2016).","journal-title":"J. Clin. Diagn. Res."},{"issue":"6","key":"24593_CR34","doi-asserted-by":"publisher","first-page":"2214","DOI":"10.1097\/SCS.0000000000001063","volume":"25","author":"N Y\u00fczba\u015fio\u011flu","year":"2014","unstructured":"Y\u00fczba\u015fio\u011flu, N., Yilmaz, M. T., \u00c7icekcibasi, A. E., \u015eeker, M. & Sakarya, M. E. The evaluation of morphometry of nasal bone and pyriform aperture using multidetector computed tomography. J. Craniofac. Surg. 25 (6), 2214\u20132219. https:\/\/doi.org\/10.1097\/SCS.0000000000001063 (2014).","journal-title":"J. Craniofac. Surg."},{"issue":"2","key":"24593_CR35","doi-asserted-by":"publisher","first-page":"393","DOI":"10.4067\/S0717-95022011000200014","volume":"29","author":"FB Prado","year":"2011","unstructured":"Prado, F. B. et al. Piriform aperture morfometry and nasal bones morphology in Brazilian population by postero-anterior caldwell radiographys. Int. J. Morphol. 29 (2), 393\u2013398. https:\/\/doi.org\/10.4067\/S0717-95022011000200014 (2011).","journal-title":"Int. J. Morphol."},{"key":"24593_CR36","unstructured":"Scientific Working Group for Forensic Anthropology (SWGANTH). Sex assessment. (2010). Available at: https:\/\/www.nist.gov\/system\/files\/documents\/2018\/03\/13\/swganth_sex_assessment.pdf"},{"key":"24593_CR37","volume-title":"Tratado De Antropologia Forense","author":"AR Deitos","year":"2022","unstructured":"Deitos, A. R. & Cunha, E. Estimativa do Sexo Em antropologia forense. In: (eds Machado, C. E. P., Deitos, A. R., Velho, J. A. & Cunha, E.) Tratado De Antropologia Forense. Campinas, SP: Millennium; p. 343-360. (2022). [Portuguese]."},{"key":"24593_CR38","doi-asserted-by":"publisher","first-page":"110441","DOI":"10.1016\/j.forsciint.2020.110441","volume":"315","author":"DH Toneva","year":"2020","unstructured":"Toneva, D. H. et al. Data mining for sex Estimation based on cranial measurements. Forensic Sci. Int. 315, 110441. https:\/\/doi.org\/10.1016\/j.forsciint.2020.110441 (2020).","journal-title":"Forensic Sci. Int."},{"issue":"1","key":"24593_CR39","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1127\/anthranz\/2023\/1733","volume":"81","author":"D Toneva","year":"2024","unstructured":"Toneva, D. et al. Sex Estimation based on mandibular measurements. Anthropol. Anz. 81 (1), 19\u201342. https:\/\/doi.org\/10.1127\/anthranz\/2023\/1733 (2024).","journal-title":"Anthropol. Anz"},{"issue":"1","key":"24593_CR40","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1053\/j.sodo.2008.09.001","volume":"15","author":"AG Farman","year":"2009","unstructured":"Farman, A. G. & Scarfe, W. C. The basics of maxillofacial cone beam computed tomography. Semin Orthod. 15 (1), 2\u201313. https:\/\/doi.org\/10.1053\/j.sodo.2008.09.001 (2009).","journal-title":"Semin Orthod."},{"key":"24593_CR41","first-page":"1","volume":"24","author":"JR Camargo","year":"2007","unstructured":"Camargo, J. R. et al. The frontal sinus morphology in radiographs of Brazilian subjects: its forensic importance. Braz J. Morphol. Sci. 24, 1\u20135 (2007).","journal-title":"Braz J. Morphol. Sci."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24593-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24593-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24593-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T12:17:11Z","timestamp":1763554631000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-24593-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,19]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["24593"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-24593-5","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,19]]},"assertion":[{"value":"9 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was carried out following the ethical standards of the Declaration of Helsinki (Finland).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}},{"value":"The outlined investigation protocol received approval from the Institutional Committee of Ethics in Human Research at the Faculdade S\u00e3o Leopoldo Mandic (Protocol No. 76809023.9.0000.5374).","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The images utilized in this study constituted secondary data sourced from an established radiology database (Center of Oral Radiology and Imaging). Access to the data was authorized through informed permission granted by the database\u2019s legal custodian, and subsequently approved by the relevant ethics committee.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"40784"}}