{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T07:25:07Z","timestamp":1787383507760,"version":"build-2736575974"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:00:00Z","timestamp":1778284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:00:00Z","timestamp":1778284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100007613","name":"Ankara University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007613","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Legal Med"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and compare two deep learning-based segmentation\u2013radiomics pipelines \u2014 YOLOv8-Hybrid and nnU-Net v2 \u2014 for automated sex classification and age estimation from maxillary sinus morphometry on panoramic radiographs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A balanced dataset of 1,024 panoramic radiographs (512 males, 512 females; age 18\u201381 years) was collected from Near East University, North Cyprus. Ground truth sinus annotations were generated by an expert oral radiologist and validated through dual-annotator inter-observer reliability assessment (ICC (2,1)\u2009=\u20090.94\u20130.97). The YOLOv8-Hybrid pipeline employed YOLOv8n-seg coarse segmentation, U-Net boundary refinement, &gt;\u2009120 morphometric and radiomic features, and CatBoost\/XGBoost classifiers. The nnU-Net v2 pipeline used auto-configured 2D U-Net segmentation with identical feature extraction and XGBoost prediction. Both pipelines underwent 5-fold cross-validation with patient-level splitting, transfer learning, Bayesian hyperparameter optimization, and SHAP interpretability analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      nnU-Net v2 achieved statistically significant superiority in sex classification (AUC\u2009=\u20090.927 [95% CI: 0.881\u20130.964]) over YOLOv8-CatBoost (AUC\u2009=\u20090.893 [0.841\u20130.938]; DeLong\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.024, Cohen\u2019s d\u2009=\u20090.48). Both pipelines demonstrated comparable age estimation performance (MAE\u2009\u2248\u20097.2 years). YOLOv8 showed exceptional consistency (mAP@50\u2009=\u200998.19%, CV\u2009=\u20090.77%). SHAP analysis identified bilateral area difference as the most determinant feature (sex: 0.42, age: 0.51). External validation on 50 independent images confirmed model generalizability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study provides the first systematic comparison of YOLOv8 and nnU-Net v2 for forensic maxillary sinus analysis. nnU-Net v2 is recommended for precision-critical forensic reporting, while YOLOv8-Hybrid is suited for high-throughput screening. The &gt;\u2009120 radiomic\/morphometric features establish a comprehensive framework for automated biological profiling.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00414-026-03825-x","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T01:11:50Z","timestamp":1778289110000},"page":"3163-3177","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A comparative analysis of YOLOv8 and nnU-Net v2 based pipelines for sex and age estimation from maxillary sinus morphometry on panoramic radiographs"],"prefix":"10.1007","volume":"140","author":[{"given":"Mert","family":"Ocak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cumali","family":"\u00c7atak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Se\u00e7il","family":"Aksoy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaan","family":"Orhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,9]]},"reference":[{"key":"3825_CR1","volume-title":"Forensic anthropology: current methods and practice","author":"AM Christensen","year":"2019","unstructured":"Christensen AM, Passalacqua NV, Bartelink EJ (2019) Forensic anthropology: current methods and practice, 2nd edn. Academic, San Diego","edition":"2"},{"key":"3825_CR2","doi-asserted-by":"publisher","DOI":"10.1201\/b10727","volume-title":"Forensic anthropology: 2000 to 2010","author":"S Black","year":"2011","unstructured":"Black S, Ferguson E (2011) Forensic anthropology: 2000 to 2010. CRC, Boca Raton"},{"key":"3825_CR3","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1002\/9781119151647.ch2","volume-title":"Biological anthropology of the human skeleton","author":"DH Ubelaker","year":"2018","unstructured":"Ubelaker DH (2018) Forensic anthropology: methodology and applications. In: Katzenberg MA, Grauer AL (eds) Biological anthropology of the human skeleton. Wiley, Hoboken, pp 43\u201371"},{"key":"3825_CR4","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1002\/ajpa.20776","volume":"136","author":"PL Walker","year":"2008","unstructured":"Walker PL (2008) Sexing skulls using discriminant function analysis. Am J Phys Anthropol 136:39\u201350","journal-title":"Am J Phys Anthropol"},{"key":"3825_CR5","first-page":"211","volume":"45","author":"A Demirjian","year":"1973","unstructured":"Demirjian A, Goldstein H, Tanner JM (1973) A new system of dental age assessment. Hum Biol 45:211\u2013227","journal-title":"Hum Biol"},{"key":"3825_CR6","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/BF02437238","volume":"5","author":"S Brooks","year":"1990","unstructured":"Brooks S, Suchey JM (1990) Skeletal age determination based on os pubis. Hum Evol 5:227\u2013238","journal-title":"Hum Evol"},{"key":"3825_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.forsciint.2009.09.008","volume":"193","author":"E Cunha","year":"2009","unstructured":"Cunha E, Baccino E, Martrille L et al (2009) The problem of aging human remains. Forensic Sci Int 193:1\u201313","journal-title":"Forensic Sci Int"},{"key":"3825_CR8","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.forsciint.2006.05.018","volume":"165","author":"C Cattaneo","year":"2007","unstructured":"Cattaneo C (2007) Forensic anthropology: developments of a classical discipline. Forensic Sci Int 165:185\u2013193","journal-title":"Forensic Sci Int"},{"key":"3825_CR9","first-page":"735","volume":"48","author":"HJ Kim","year":"2003","unstructured":"Kim HJ, Song HY, Han CK et al (2003) Sex determination using maxillary sinus. J Forensic Sci 48:735\u2013738","journal-title":"J Forensic Sci"},{"key":"3825_CR10","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1111\/j.1556-4029.2010.01642.x","volume":"56","author":"AT Uthman","year":"2011","unstructured":"Uthman AT, Al-Rawi NH, Al-Naaimi AS et al (2011) Evaluation of maxillary sinus dimensions in gender determination using helical CT. J Forensic Sci 56:403\u2013408","journal-title":"J Forensic Sci"},{"key":"3825_CR11","first-page":"251","volume":"8","author":"AT Uthman","year":"2012","unstructured":"Uthman AT, Al-Rawi NH, Al-Timimi JF (2012) Maxillary sinus dimensions in gender determination using helical CT scanning. Forensic Sci Med Pathol 8:251\u2013259","journal-title":"Forensic Sci Med Pathol"},{"key":"3825_CR12","first-page":"ZC67","volume":"11","author":"A Urooge","year":"2017","unstructured":"Urooge A, Patil BA (2017) Sexual dimorphism of maxillary sinus. J Clin Diagn Res 11:ZC67\u2013ZC71","journal-title":"J Clin Diagn Res"},{"key":"3825_CR13","doi-asserted-by":"publisher","first-page":"S11","DOI":"10.1111\/j.1834-7819.2008.00037.x","volume":"53","author":"PA Monsour","year":"2008","unstructured":"Monsour PA, Dudhia R (2008) Implant radiography and radiology. Aust Dent J 53:S11\u2013S25","journal-title":"Aust Dent J"},{"key":"3825_CR14","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749\u2013762","journal-title":"Nat Rev Clin Oncol"},{"key":"3825_CR15","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563\u2013577","journal-title":"Radiology"},{"key":"3825_CR16","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"3825_CR17","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444","journal-title":"Nature"},{"key":"3825_CR18","doi-asserted-by":"crossref","first-page":"9432","DOI":"10.1038\/s41598-019-45849-x","volume":"9","author":"J Bewes","year":"2019","unstructured":"Bewes J, Low A, Morphett A et al (2019) Artificial intelligence for sex estimation based on the skull. Sci Rep 9:9432","journal-title":"Sci Rep"},{"key":"3825_CR19","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R et al (2016) You only look once: unified real-time object detection. In: Proceedings of the IEEE CVPR, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"3825_CR20","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) YOLOv4: optimal speed and accuracy. arXiv preprint arXiv:2004.10934"},{"key":"3825_CR21","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.3390\/make5040083","volume":"5","author":"J Terven","year":"2023","unstructured":"Terven J, Cordova-Esparza D (2023) A comprehensive review of YOLO architectures. Mach Learn Knowl Extr 5:1680\u20131716","journal-title":"Mach Learn Knowl Extr"},{"key":"3825_CR22","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SAA et al (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18:203\u2013211","journal-title":"Nat Methods"},{"key":"3825_CR23","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR et al (2016) Convolutional neural networks for medical image analysis. IEEE Trans Med Imaging 35:1299\u20131312","journal-title":"IEEE Trans Med Imaging"},{"key":"3825_CR24","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3825_CR25","first-page":"3347","volume":"32","author":"M Raghu","year":"2019","unstructured":"Raghu M, Zhang C, Kleinberg J et al (2019) Transfusion: understanding transfer learning for medical imaging. Adv NeurIPS 32:3347\u20133357","journal-title":"Adv NeurIPS"},{"key":"3825_CR26","doi-asserted-by":"publisher","unstructured":"Zhou Z, Sodha V, Rahman Siddiquee MM, Feng R, Tajbakhsh N, Gotway MB, Liang J (2019) Models genesis: Generic autodidactic models for 3d medical image analysis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp. 384\u2013393. https:\/\/doi.org\/10.1007\/978-3-030-32251-9_42","DOI":"10.1007\/978-3-030-32251-9_42"},{"key":"3825_CR27","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE CVPR, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3825_CR28","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.jasi.2014.04.007","volume":"63","author":"SK Sharma","year":"2014","unstructured":"Sharma SK, Jehan M, Kumar A (2014) Measurements of maxillary sinus volume by CT for gender determination. J Anat Soc India 63:36\u201342","journal-title":"J Anat Soc India"},{"key":"3825_CR29","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1111\/j.1556-4029.2008.00851.x","volume":"53","author":"R Cameriere","year":"2008","unstructured":"Cameriere R, Ferrante L, Cingolani M (2008) Age estimation by pulp\/tooth area ratio. J Forensic Sci 53:1151\u20131155","journal-title":"J Forensic Sci"},{"key":"3825_CR30","doi-asserted-by":"crossref","first-page":"160e1","DOI":"10.1016\/j.forsciint.2016.01.026","volume":"261","author":"A Gulsahi","year":"2016","unstructured":"Gulsahi A, Yuzugullu B, Imirzalioglu P et al (2016) Age estimation based on pulp\/tooth area ratio (CBCT). Forensic Sci Int 261:160e1\u2013160e5","journal-title":"Forensic Sci Int"},{"key":"3825_CR31","first-page":"1","volume":"51","author":"M Paknahad","year":"2017","unstructured":"Paknahad M, Shahidi S, Hedayati Azar H (2017) Sexual dimorphism of maxillary sinus dimensions using CBCT. J Forensic Leg Med 51:1\u20136","journal-title":"J Forensic Leg Med"},{"key":"3825_CR32","first-page":"6638","volume":"31","author":"L Prokhorenkova","year":"2018","unstructured":"Prokhorenkova L, Gusev G, Vorobev A et al (2018) CatBoost: unbiased boosting with categorical features. Adv NeurIPS 31:6638\u20136648","journal-title":"Adv NeurIPS"},{"key":"3825_CR33","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the KDD, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"3825_CR34","doi-asserted-by":"crossref","unstructured":"Akiba T, Sano S, Yanase T et al (2019) Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the KDD, pp 2623\u20132631","DOI":"10.1145\/3292500.3330701"},{"key":"3825_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-4541-9","volume-title":"An introduction to the bootstrap","author":"B Efron","year":"1993","unstructured":"Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall, New York"},{"key":"3825_CR36","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing areas under ROC curves. Biometrics 44:837\u2013845","journal-title":"Biometrics"},{"key":"3825_CR37","volume-title":"Statistical power analysis for the behavioral sciences","author":"J Cohen","year":"1988","unstructured":"Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum, Hillsdale","edition":"2"},{"key":"3825_CR38","first-page":"4765","volume":"30","author":"SM Lundberg","year":"2017","unstructured":"Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Adv NeurIPS 30:4765\u20134774","journal-title":"Adv NeurIPS"},{"key":"3825_CR39","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","volume":"327","author":"JM Bland","year":"1986","unstructured":"Bland JM, Altman DG (1986) Statistical methods for assessing agreement. Lancet 327:307\u2013310","journal-title":"Lancet"},{"key":"3825_CR40","doi-asserted-by":"crossref","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2023) YOLOv7: trainable bag-of-freebies. In: Proceedings of the IEEE CVPR, pp 7464\u20137475","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"3825_CR41","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1002\/ca.20813","volume":"22","author":"R Koymen","year":"2009","unstructured":"Koymen R, Gocmen-Mas N, Karacayli U et al (2009) Anatomic evaluation of maxillary sinus septa. Clin Anat 22:563\u2013570","journal-title":"Clin Anat"},{"key":"3825_CR42","doi-asserted-by":"publisher","first-page":"2374","DOI":"10.1109\/TMI.2020.2968765","volume":"39","author":"N Vila-Blanco","year":"2020","unstructured":"Vila-Blanco N, Carreira MJ, Varas-Quintana P et al (2020) Deep neural networks for chronological age estimation from OPG. IEEE Trans Med Imaging 39:2374\u20132384","journal-title":"IEEE Trans Med Imaging"},{"key":"3825_CR43","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1111\/1556-4029.14182","volume":"65","author":"R Merdietio Boedi","year":"2020","unstructured":"Merdietio Boedi R, Banar N, De Tobel J et al (2020) Effect of lower third molar segmentations on automated tooth development staging. J Forensic Sci 65:481\u2013486","journal-title":"J Forensic Sci"},{"key":"3825_CR44","unstructured":"Roboflow Universe (2023) Teeth detection dataset. https:\/\/universe.roboflow.com\/teeth-detection. Accessed 15 Mar 2024"},{"key":"3825_CR45","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/1746-160X-5-19","volume":"5","author":"E Puricelli","year":"2009","unstructured":"Puricelli E (2009) Panorametry: suggestion of a method for mandibular measurements. Head Face Med 5:19","journal-title":"Head Face Med"},{"key":"3825_CR46","doi-asserted-by":"publisher","first-page":"20190205","DOI":"10.1259\/dmfr.20190205","volume":"48","author":"A Whyte","year":"2019","unstructured":"Whyte A, Boeddinghaus R (2019) The maxillary sinus: physiology, development and imaging anatomy. Dentomaxillofac Radiol 48:20190205","journal-title":"Dentomaxillofac Radiol"},{"key":"3825_CR47","doi-asserted-by":"publisher","first-page":"17","DOI":"10.5115\/acb.2019.52.1.17","volume":"52","author":"J Iwanaga","year":"2019","unstructured":"Iwanaga J, Wilson C, Lachkar S et al (2019) Clinical anatomy of the maxillary sinus. Anat Cell Biol 52:17\u201324","journal-title":"Anat Cell Biol"},{"key":"3825_CR48","first-page":"e180","volume":"30","author":"V Divyadharsini","year":"2023","unstructured":"Divyadharsini V, Uma Maheswari TN (2023) Assessment of adult maxillary sinus dimension for sex determination. J Popul Ther Clin Pharmacol 30:e180\u2013e186","journal-title":"J Popul Ther Clin Pharmacol"},{"key":"3825_CR49","doi-asserted-by":"publisher","first-page":"215","DOI":"10.15644\/asc50\/3\/3","volume":"50","author":"CL de Queiroz","year":"2016","unstructured":"de Queiroz CL, Terada ASSD, Dezem TU et al (2016) Sex determination of adult human maxillary sinuses on panoramic radiographs. Acta Stomatol Croat 50:215\u2013221","journal-title":"Acta Stomatol Croat"},{"key":"3825_CR50","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1037\/1040-3590.6.4.284","volume":"6","author":"DV Cicchetti","year":"1994","unstructured":"Cicchetti DV (1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 6:284\u2013290","journal-title":"Psychol Assess"},{"key":"3825_CR51","unstructured":"Jocher G, Chaurasia A, Qiu J (2023) Ultralytics YOLOv8. https:\/\/github.com\/ultralytics\/ultralytics. Accessed 10 Jan 2025"},{"key":"3825_CR52","doi-asserted-by":"publisher","unstructured":"Isensee F, Wald T, Ulrich C, Baumgartner M, Roy S, Maier-Hein K, Jaeger PF (2024) nnU-Net revisited: A call for rigorous validation in 3D medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer Nature, Cham, pp 488\u2013498. https:\/\/doi.org\/10.48550\/arXiv.1505.0459710.1007\/978-3-031-72114-4_47","DOI":"10.48550\/arXiv.1505.0459710.1007\/978-3-031-72114-4_47"},{"key":"3825_CR53","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N et al (2023) Segment anything. In: Proceedings of the IEEE\/CVF ICCV, pp 4015\u20134026","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"3825_CR54","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41592-023-02151-z","volume":"21","author":"L Maier-Hein","year":"2024","unstructured":"Maier-Hein L, Reinke A, Godau P et al (2024) Metrics reloaded: recommendations for image analysis validation. Nat Methods 21:195\u2013212","journal-title":"Nat Methods"},{"key":"3825_CR55","first-page":"1893","volume":"17","author":"L Kats","year":"2022","unstructured":"Kats L, Vered M, Zlotogorski-Hurvitz A et al (2022) Artificial intelligence in forensic medicine and forensic dentistry: current state of the art. J Dent Sci 17:1893\u20131899","journal-title":"J Dent Sci"},{"key":"3825_CR56","first-page":"1","volume":"55","author":"B Alsinglawi","year":"2022","unstructured":"Alsinglawi B, Alghamdi A, Mubin O et al (2022) An explainable machine learning framework for human age and gender detection. ACM Comput Surv 55:1\u201337","journal-title":"ACM Comput Surv"},{"key":"3825_CR57","first-page":"123","volume":"103","author":"SB Khanagar","year":"2024","unstructured":"Khanagar SB, Alsaquib S, Albalawi F et al (2024) Developments and performance of AI in dentistry. J Dent Res 103:123\u2013135","journal-title":"J Dent Res"},{"key":"3825_CR58","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1111\/1556-4029.15400","volume":"69","author":"BO Gurses","year":"2024","unstructured":"Gurses BO, Alpoz E, \u00d6nem E et al (2024) Maxillary sinus measurement-based sex estimation using machine learning. J Forensic Sci 69:282\u2013289","journal-title":"J Forensic Sci"},{"key":"3825_CR59","doi-asserted-by":"crossref","first-page":"6521","DOI":"10.1007\/s00330-023-09859-8","volume":"33","author":"S Tian","year":"2023","unstructured":"Tian S, Liu A, Wang H et al (2023) Radiomics-based machine learning for outcome prediction in a multicenter study. Eur Radiol 33:6521\u20136531","journal-title":"Eur Radiol"},{"key":"3825_CR60","doi-asserted-by":"crossref","first-page":"111835","DOI":"10.1016\/j.forsciint.2023.111835","volume":"352","author":"A Franco","year":"2023","unstructured":"Franco A, Vodovotz L, Wannmacher CLS et al (2023) Sex estimation from maxillary sinuses: a systematic review. Forensic Sci Int 352:111835","journal-title":"Forensic Sci Int"},{"key":"3825_CR61","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104\u2013e107","journal-title":"Cancer Res"},{"key":"3825_CR62","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA et al (2020) The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328\u2013338","journal-title":"Radiology"}],"container-title":["International Journal of Legal Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00414-026-03825-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00414-026-03825-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00414-026-03825-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T07:02:17Z","timestamp":1787382137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00414-026-03825-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,9]]},"references-count":62,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["3825"],"URL":"https:\/\/doi.org\/10.1007\/s00414-026-03825-x","relation":{},"ISSN":["0937-9827","1437-1596"],"issn-type":[{"value":"0937-9827","type":"print"},{"value":"1437-1596","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,9]]},"assertion":[{"value":"11 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2026","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original version of this paper has been revised to update affiliation 5.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was conducted in accordance with the Declaration of Helsinki, and approved by the Near East University Scientific Research Ethics Committee (IRB\/Ethics Committee; decision no. YDU\/2025\/139\u20132045, dated 05.01.2026). Due to the retrospective design of the study and the complete de-identification of the data, the requirement for individual patient consent was waived by the Near East University Scientific Research Ethics Committee.","order":2,"name":"Ethics","label":"Institutional review board statement","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study is based on retrospective analysis of fully anonymized panoramic radiographs. The Near East University Scientific Research Ethics Committee waived the requirement for individual participant consent due to the retrospective design and complete de-identification of all patient data (decision no. YDU\/2025\/139\u20132045, dated 05.01.2026).","order":3,"name":"Ethics","label":"Consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Near East University Scientific Research Ethics Committee (decision no. YDU\/2025\/139\u20132045, dated 05.01.2026). The study was conducted in accordance with the principles of the Declaration of Helsinki. Due to the retrospective design and complete de-identification of all data, the requirement for individual patient consent was waived by the ethics committee in accordance with institutional standards. No identifiable patient information was used.","order":4,"name":"Ethics","label":"Human ethics and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable","order":5,"name":"Ethics","label":"Clinical trial registration","group":{"name":"EthicsHeading","label":"Declarations"}}]}}