{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:09:27Z","timestamp":1793275767225,"version":"4.5.0"},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["0001"],"award-info":[{"award-number":["0001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["0001"],"award-info":[{"award-number":["0001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"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>Objective<\/jats:title>\n            <jats:p>To assess the performance of cephalometric landmark identification performed by two AI-driven software programs in images from different populations (Brazilian and Korean).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>Sixty lateral cephalometric radiographs (30 Brazilian and 30 Korean) were analyzed. The Brazilian images were acquired using the Orthophos XG 5\/Ceph device, while the Korean images were obtained from the International Symposium on Biomedical Imaging 2015 database. Images of patients with permanent dentition were included, excluding those with poor head positioning or severe craniofacial deformities. Twenty cephalometric landmarks were identified by two examiners used as the reference standard. Two AI-driven software programs, CefBot\u2122 (Brazil) and WebCeph\u2122 (Korea), automatically identified the same landmarks. Coordinate values for each landmark were measured using ImageJ, and the data were analyzed with Analysis of Variance and Dunnett\u2019s post-hoc test.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The Brazilian software showed high accuracy in identifying landmarks on Brazilian images (90%) but was less precise on Korean images (80%), with significant discrepancies in the Glabella, Menton L, Basion, and Orbitale landmarks. Similarly, the Korean software had a higher accuracy in its own population (95%) than in another population (85%), with notable inaccuracies in the Menton L, Basion, and Porion landmarks.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Discrepancies in the identification of specific landmarks, such as Glabella and Menton L, suggest that the accuracy of the software may be influenced by the training process itself and by the population origin of the training data.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12903-025-06807-4","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T08:27:54Z","timestamp":1760084874000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Populational influence on cephalometric landmark identification: performance of two AI-driven software programs in Brazilian and Korean images"],"prefix":"10.1186","volume":"25","author":[{"given":"Thaisa Pinheiro","family":"Silva","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanna Sachs","family":"Puntigam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Fernanda Silva","family":"Andrade-Bortoletto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wilton Mitsunari","family":"Takeshita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christiano","family":"Oliveira-Santos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deborah Queiroz","family":"Freitas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"6807_CR1","doi-asserted-by":"publisher","first-page":"0036","DOI":"10.1038\/s41551-017-0036","volume":"1","author":"N Editors","year":"2017","unstructured":"Editors N. Auspicious machine learning. Nat Biomed Eng. 2017;1:0036.","journal-title":"Nat Biomed Eng"},{"issue":"3 Pt B","key":"6807_CR2","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.jacr.2017.12.026","volume":"15","author":"JH Thrall","year":"2018","unstructured":"Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, Brink J. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol. 2018;15(3 Pt B):504\u20138.","journal-title":"J Am Coll Radiol"},{"key":"6807_CR3","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T. Recent advances in convolutional neural networks. Pattern Recognit. 2018;77:354\u201377. https:\/\/doi.org\/10.1016\/j.patcog.2017.10.013.","journal-title":"Pattern Recognit"},{"key":"6807_CR4","unstructured":"Data Science Academy. Deep Learning Book. 2023. Available from: https:\/\/www.deeplearningbook.com.br\/machine-learning-guia-definitivo-parte-3\/. Accessed April 8, 2023."},{"issue":"9","key":"6807_CR5","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1109\/TMI.2015.2412951","volume":"34","author":"CW Wang","year":"2015","unstructured":"Wang CW, Huang CT, Hsieh MC. Evaluation and comparison of anatomical landmark detection methods for cephalometric X-ray images. IEEE Trans Med Imaging. 2015;34(9):1890\u2013900.","journal-title":"IEEE Trans Med Imaging"},{"key":"6807_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/srep33581","volume":"6","author":"C Lindner","year":"2016","unstructured":"Lindner C, Wang CW, Huang CT, Li CH, Chang SW, Cootes TF. Fully automatic system for accurate localisation and analysis of cephalometric landmarks in lateral cephalograms. Sci Rep. 2016;6: 33581. https:\/\/doi.org\/10.1038\/srep33581.","journal-title":"Sci Rep"},{"issue":"1","key":"6807_CR7","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.4.1.014501","volume":"4","author":"S\u00d6 Arik","year":"2017","unstructured":"Arik S\u00d6, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging. 2017;4(1): 014501. https:\/\/doi.org\/10.1117\/1.JMI.4.1.014501.","journal-title":"J Med Imaging"},{"issue":"6","key":"6807_CR8","doi-asserted-by":"publisher","first-page":"903","DOI":"10.2319\/022019-127.1","volume":"89","author":"JH Park","year":"2019","unstructured":"Park JH, Hwang HW, Moon JH. Automated identification of cephalometric landmarks: part 1\u2014comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod. 2019;89(6):903\u20139.","journal-title":"Angle Orthod"},{"issue":"1","key":"6807_CR9","doi-asserted-by":"publisher","first-page":"69","DOI":"10.2319\/022019-129.1","volume":"90","author":"HW Hwang","year":"2020","unstructured":"Hwang HW, Park JH, Moon JH. Automated identification of cephalometric landmarks: part 2\u2014might it be better than human? Angle Orthod. 2020;90(1):69\u201376.","journal-title":"Angle Orthod"},{"key":"6807_CR10","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/ocr.12542","volume":"24","author":"G Bulatova","year":"2021","unstructured":"Bulatova G, Kusnoto B, Grace V, Tsay TP, Avenetti DM, Sanchez FJC. Assessment of automatic cephalometric landmark identification using artificial intelligence. Orthod Craniofac Res. 2021;24:37\u201342. https:\/\/doi.org\/10.1111\/ocr.12542.","journal-title":"Orthod Craniofac Res"},{"issue":"1","key":"6807_CR11","doi-asserted-by":"publisher","first-page":"20200548","DOI":"10.1259\/dmfr.20200548","volume":"50","author":"TP Silva","year":"2021","unstructured":"Silva TP, Hughes MM, Menezes L, dos Melo S, de F M, B de, Takeshita WM, de Freitas PHL. Artificial intelligence-based cephalometric landmark annotation and measurements according to arnett\u2019s analysis: can we trust a bot to do that? Dentomaxillofac Radiol. 2021;50(1):20200548. https:\/\/doi.org\/10.1259\/dmfr.20200548.","journal-title":"Dentomaxillofac Radiol"},{"issue":"8","key":"6807_CR12","doi-asserted-by":"publisher","first-page":"20230065","DOI":"10.1259\/dmfr.20230065","volume":"52","author":"LDS Menezes","year":"2023","unstructured":"Menezes LDS, Silva TP, Lima Dos Santos MA, Hughes MM, Mariano Souza SDR, Leite Ribeiro PM, Freitas PHL, Takeshita WM. Assessment of landmark detection in cephalometric radiographs with different conditions of brightness and contrast using an artificial intelligence software. Dentomaxillofac Radiol. 2023;52(8):20230065. https:\/\/doi.org\/10.1259\/dmfr.20230065. Epub 2023 Oct 23. PMID: 37869886; PMCID: PMC10968755.","journal-title":"Dentomaxillofac Radiol"},{"issue":"4","key":"6807_CR13","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.oooo.2024.01.011","volume":"137","author":"TP Silva","year":"2024","unstructured":"Silva TP, Pinheiro MCR, Freitas DQ, Ga\u00eata-Araujo H, Oliveira-Santos C. Assessment of accuracy and reproducibility of cephalometric identification performed by 2 artificial intelligence-driven tracing applications and human examiners. Oral Surg Oral Med Oral Pathol Oral Radiol. 2024;137(4):431\u201340. Epub 2024 Jan 22. PMID: 38365543.","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"issue":"1","key":"6807_CR14","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.jtumed.2021.05.005","volume":"16","author":"YA Yassir","year":"2021","unstructured":"Yassir YA, Salman AR, Nabbat SA. The accuracy and reliability of webceph for cephalometric analysis. J Taibah Univ Med Sci. 2021;16(1):165\u2013836. https:\/\/doi.org\/10.1016\/j.jtumed.2021.05.005.","journal-title":"J Taibah Univ Med Sci"},{"issue":"1","key":"6807_CR15","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-023-02881-8","volume":"23","author":"H Bao","year":"2023","unstructured":"Bao H, Zhang K, Yu C, Li H, Cao D, Shu H, Liu L, Yan B. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health. 2023;23(1): 191. https:\/\/doi.org\/10.1186\/s12903-023-02881-8.","journal-title":"BMC Oral Health"},{"issue":"12","key":"6807_CR16","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11121286","volume":"11","author":"J Ribas-Sabart\u00e9s","year":"2024","unstructured":"Ribas-Sabart\u00e9s J, S\u00e1nchez-Molins M, d\u2019Oliveira NG. The accuracy of algorithms used by artificial intelligence in cephalometric points detection: a systematic review. Bioengineering. 2024;11(12): 1286. https:\/\/doi.org\/10.3390\/bioengineering11121286.","journal-title":"Bioengineering"}],"container-title":["BMC Oral Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-025-06807-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12903-025-06807-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12903-025-06807-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T12:13:30Z","timestamp":1760357610000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcoralhealth.biomedcentral.com\/articles\/10.1186\/s12903-025-06807-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,10]]},"references-count":16,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6807"],"URL":"https:\/\/doi.org\/10.1186\/s12903-025-06807-4","relation":{},"ISSN":["1472-6831"],"issn-type":[{"value":"1472-6831","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,10]]},"assertion":[{"value":"5 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 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":"This study was registered and approved by the local Institutional Review Board (Piracicaba Dental School) under the protocol number CAAE 71615723.0.0000.5418 and was carried out according to the Declaration of Helsinki. All Brazilian individuals assigned an informed consent for their radiographs to be included in the study.","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 to publish"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1596"}}