{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T16:18:39Z","timestamp":1791994719353,"version":"4.4.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002635","name":"Inha University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002635","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Oral Radiol"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s11282-026-00921-x","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T19:38:08Z","timestamp":1776454688000},"page":"801-809","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated periodontitis diagnosis and staging using an end-to-end deep learning model on panoramic dental radiographs"],"prefix":"10.1007","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3221-3804","authenticated-orcid":false,"given":"My Huong","family":"Le","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8613-7029","authenticated-orcid":false,"given":"Xuan Hao","family":"Mai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Batzaya","family":"Tumur-Ulzii","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4272-3615","authenticated-orcid":false,"given":"So-Hyun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0155-7746","authenticated-orcid":false,"given":"Nam-Sik","family":"Oh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"921_CR1","doi-asserted-by":"publisher","first-page":"S173","DOI":"10.1002\/JPER.17-0721","volume":"89","author":"PN Papapanou","year":"2018","unstructured":"Papapanou PN, Sanz M, Buduneli N, et al. Periodontitis: consensus report of workgroup 2 of the 2017 World Workshop on the classification of periodontal and peri\u2010implant diseases and conditions. J Periodontol. 2018;89:S173\u201382. https:\/\/doi.org\/10.1002\/JPER.17-0721.","journal-title":"J Periodontol"},{"issue":"1","key":"921_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/nrdp.2017.38","volume":"3","author":"DF Kinane","year":"2017","unstructured":"Kinane DF, Stathopoulou PG, Papapanou P. Periodontal diseases. Nat Rev Dis Primers. 2017;3(1):1\u201314.","journal-title":"Nat Rev Dis Primers"},{"key":"921_CR3","doi-asserted-by":"publisher","DOI":"10.1111\/prd.12536","author":"C Marruganti","year":"2023","unstructured":"Marruganti C, Suvan JE, D\u2019Aiuto F. Periodontitis and metabolic diseases (diabetes and obesity): tackling multimorbidity. Periodontol 2000. 2023. https:\/\/doi.org\/10.1111\/prd.12536.","journal-title":"Periodontol 2000"},{"key":"921_CR4","doi-asserted-by":"publisher","DOI":"10.1111\/prd.12487","author":"GE Salvi","year":"2023","unstructured":"Salvi GE, Roccuzzo A, Imber JC, Stahli A, Klinge B, Lang NP. Clinical periodontal diagnosis. Periodontol 2000. 2023. https:\/\/doi.org\/10.1111\/prd.12487.","journal-title":"Periodontol 2000"},{"issue":"1","key":"921_CR5","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1111\/prd.12580","volume":"95","author":"R Jacobs","year":"2024","unstructured":"Jacobs R, Fontenele RC, Lahoud P, Shujaat S, Bornstein MM. Radiographic diagnosis of periodontal diseases\u2014current evidence versus innovations. Periodontol 2000. 2024;95(1):51\u201369. https:\/\/doi.org\/10.1111\/prd.12580.","journal-title":"Periodontol 2000"},{"key":"921_CR6","unstructured":"Affairs Ada-CoS Dental radiographic examinations: recommendations for patient selection and limiting radiation exposure, USA; 2012."},{"key":"921_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2024.105373","volume":"150","author":"T Xue","year":"2024","unstructured":"Xue T, Chen L, Sun Q. Deep learning method to automatically diagnose periodontal bone loss and periodontitis stage in dental panoramic radiograph. J Dent. 2024;150:105373. https:\/\/doi.org\/10.1016\/j.jdent.2024.105373.","journal-title":"J Dent"},{"issue":"1","key":"921_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8(1):53. https:\/\/doi.org\/10.1186\/s40537-021-00444-8.","journal-title":"J Big Data"},{"issue":"12","key":"921_CR9","doi-asserted-by":"publisher","first-page":"3200","DOI":"10.3390\/biomedicines11123200","volume":"11","author":"E Yang","year":"2023","unstructured":"Yang E, Shankar K, Kumar S, Seo C, Moon I. Equilibrium optimization algorithm with deep learning enabled prostate cancer detection on MRI images. Biomedicines. 2023;11(12):3200. https:\/\/doi.org\/10.3390\/biomedicines11123200.","journal-title":"Biomedicines"},{"issue":"9","key":"921_CR10","doi-asserted-by":"publisher","first-page":"6054","DOI":"10.1007\/s00330-023-09568-2","volume":"33","author":"Q Yu","year":"2023","unstructured":"Yu Q, Ning Y, Wang A, et al. Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study. Eur Radiol. 2023;33(9):6054\u201365. https:\/\/doi.org\/10.1007\/s00330-023-09568-2.","journal-title":"Eur Radiol"},{"key":"921_CR11","doi-asserted-by":"publisher","DOI":"10.5114\/aoms\/208224","author":"M Xu","year":"2025","unstructured":"Xu M, Mei Y, Liu C, et al. Deep learning-based multimodal risk stratification for atherosclerosis management. Arch Med Sci. 2025. https:\/\/doi.org\/10.5114\/aoms\/208224.","journal-title":"Arch Med Sci"},{"issue":"1","key":"921_CR12","doi-asserted-by":"publisher","first-page":"20200171","DOI":"10.1259\/dmfr.20200171","volume":"50","author":"R Kuwana","year":"2021","unstructured":"Kuwana R, Ariji Y, Fukuda M, et al. Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs. Dentomax Radiol. 2021;50(1):20200171. https:\/\/doi.org\/10.1259\/dmfr.20200171.","journal-title":"Dentomax Radiol"},{"key":"921_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2020.103425","volume":"100","author":"AG Cantu","year":"2020","unstructured":"Cantu AG, Gehrung S, Krois J, et al. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent. 2020;100:103425. https:\/\/doi.org\/10.1016\/j.jdent.2020.103425.","journal-title":"J Dent"},{"issue":"7","key":"921_CR14","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1016\/j.joen.2019.03.016","volume":"45","author":"T Ekert","year":"2019","unstructured":"Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions. J Endod. 2019;45(7):917-922 e915. https:\/\/doi.org\/10.1016\/j.joen.2019.03.016.","journal-title":"J Endod"},{"key":"921_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jds.2025.08.015","author":"Y-C Mao","year":"2025","unstructured":"Mao Y-C, Chen C-A, Lin Y-J, et al. Automated assessment of peri-implant disease severity by deep learning and image processing in periapical radiographs. J Dent Sci. 2025. https:\/\/doi.org\/10.1016\/j.jds.2025.08.015.","journal-title":"J Dent Sci"},{"key":"921_CR16","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proc. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"issue":"1","key":"921_CR17","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1186\/s12903-022-02119-z","volume":"22","author":"L Jiang","year":"2022","unstructured":"Jiang L, Chen D, Cao Z, Wu F, Zhu H, Zhu F. A two-stage deep learning architecture for radiographic staging of periodontal bone loss. BMC Oral Health. 2022;22(1):106. https:\/\/doi.org\/10.1186\/s12903-022-02119-z.","journal-title":"BMC Oral Health"},{"key":"921_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/odi.15269","volume":"0","author":"X Li","year":"2025","unstructured":"Li X, Chen K, Zhao D, et al. Deep learning for staging periodontitis using panoramic radiographs. Oral Dis. 2025;0:1\u20139. https:\/\/doi.org\/10.1111\/odi.15269.","journal-title":"Oral Dis"},{"issue":"9","key":"921_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-025-11253-3","volume":"58","author":"R Sapkota","year":"2025","unstructured":"Sapkota R, Flores-Calero M, Qureshi R, et al. YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series. Artif Intell Rev. 2025;58(9):274.","journal-title":"Artif Intell Rev"},{"issue":"1","key":"921_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-53758-2","volume":"9","author":"J Kim","year":"2019","unstructured":"Kim J, Lee HS, Song IS, Jung KH. DeNTNet: deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep. 2019;9(1):17615. https:\/\/doi.org\/10.1038\/s41598-019-53758-2.","journal-title":"Sci Rep"},{"key":"921_CR21","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2410.17725","author":"R Khanam","year":"2024","unstructured":"Khanam R, Hussain M. Yolov11: an overview of the key architectural enhancements. arXiv. 2024. https:\/\/doi.org\/10.48550\/arXiv.2410.17725.","journal-title":"arXiv"},{"key":"921_CR22","doi-asserted-by":"crossref","unstructured":"Muramatsu C, Sakai K, Seino Y, et al. (2023) Recognition of tooth numbers and conditions on dental panoramic radiographs for assisting dental chart filing. In: Proc. Medical Imaging 2023: Computer-Aided Diagnosis, pp. 773\u2013778.","DOI":"10.1117\/12.2655306"},{"issue":"1","key":"921_CR23","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01338-w","volume":"24","author":"B Beser","year":"2024","unstructured":"Beser B, Reis T, Berber MN, 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. https:\/\/doi.org\/10.1186\/s12880-024-01338-w.","journal-title":"BMC Med Imaging"},{"issue":"3","key":"921_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10030279","volume":"10","author":"R Padilla","year":"2021","unstructured":"Padilla R, Passos WL, Dias TL, Netto SL, Da Silva E. A comparative analysis of object detection metrics with a companion open-source toolkit. Electronics Basel. 2021;10(3):279. https:\/\/doi.org\/10.3390\/electronics10030279.","journal-title":"Electronics Basel"},{"issue":"10","key":"921_CR25","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.3390\/diagnostics13101800","volume":"13","author":"BC UzunSaylan","year":"2023","unstructured":"UzunSaylan BC, Baydar O, Yesilova E, et al. Assessing the effectiveness of artificial intelligence models for detecting alveolar bone loss in periodontal disease: a panoramic radiograph study. Diagnos (Basel). 2023;13(10):1800. https:\/\/doi.org\/10.3390\/diagnostics13101800.","journal-title":"Diagnos (Basel)"},{"issue":"4","key":"921_CR26","first-page":"207","volume":"67","author":"DC Matthews","year":"2001","unstructured":"Matthews DC, Smith CG, Hanscom SL. Tooth loss in periodontal patients. J Can Dent Assoc. 2001;67(4):207\u201310.","journal-title":"J Can Dent Assoc"},{"issue":"3 Suppl 1","key":"921_CR27","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1111\/j.1875-595x.1998.tb00711.x","volume":"48","author":"G Ong","year":"1998","unstructured":"Ong G. Periodontal disease and tooth loss. Int Dent J. 1998;48(3 Suppl 1):233\u20138. https:\/\/doi.org\/10.1111\/j.1875-595x.1998.tb00711.x.","journal-title":"Int Dent J"},{"issue":"4","key":"921_CR28","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/s0300-5712(99)00062-7","volume":"28","author":"A Tugnait","year":"2000","unstructured":"Tugnait A, Clerehugh V, Hirschmann PN. The usefulness of radiographs in diagnosis and management of periodontal diseases: a review. J Dent. 2000;28(4):219\u201326. https:\/\/doi.org\/10.1016\/s0300-5712(99)00062-7.","journal-title":"J Dent"},{"issue":"1","key":"921_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-44839-3","volume":"9","author":"J Krois","year":"2019","unstructured":"Krois J, Ekert T, Meinhold L, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 2019;9(1):8495. https:\/\/doi.org\/10.1038\/s41598-019-44839-3.","journal-title":"Sci Rep"},{"issue":"7","key":"921_CR30","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.3390\/jcm9072313","volume":"9","author":"V Machado","year":"2020","unstructured":"Machado V, Proenca L, Morgado M, Mendes JJ, Botelho J. Accuracy of panoramic radiograph for diagnosing periodontitis comparing to clinical examination. J Clin Med. 2020;9(7):2313. https:\/\/doi.org\/10.3390\/jcm9072313.","journal-title":"J Clin Med"},{"issue":"1","key":"921_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13244-014-0379-4","volume":"6","author":"A Suomalainen","year":"2015","unstructured":"Suomalainen A, Pakbaznejad Esmaeili E, Robinson S. Dentomaxillofacial imaging with panoramic views and cone beam CT. Insights Imaging. 2015;6(1):1\u201316. https:\/\/doi.org\/10.1007\/s13244-014-0379-4.","journal-title":"Insights Imaging"},{"issue":"1","key":"921_CR32","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-021-01460-z","volume":"21","author":"G Berghuis","year":"2021","unstructured":"Berghuis G, Cosyn J, De Bruyn H, Hommez G, Dierens M, Christiaens V. A controlled study on the diagnostic accuracy of panoramic and peri-apical radiography for detecting furcation involvement. BMC Oral Health. 2021;21(1):115. https:\/\/doi.org\/10.1186\/s12903-021-01460-z.","journal-title":"BMC Oral Health"},{"issue":"17","key":"921_CR33","doi-asserted-by":"publisher","DOI":"10.3390\/app11177858","volume":"11","author":"R Izzetti","year":"2021","unstructured":"Izzetti R, Nisi M, Aringhieri G, Crocetti L, Graziani F, Nardi C. Basic knowledge and new advances in panoramic radiography imaging techniques: a narrative review on what dentists and radiologists should know. Appl Sci. 2021;11(17):7858. https:\/\/doi.org\/10.3390\/app11177858.","journal-title":"Appl Sci"}],"container-title":["Oral Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11282-026-00921-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11282-026-00921-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11282-026-00921-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T17:36:18Z","timestamp":1782236178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11282-026-00921-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,17]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["921"],"URL":"https:\/\/doi.org\/10.1007\/s11282-026-00921-x","relation":{},"ISSN":["0911-6028","1613-9674"],"issn-type":[{"value":"0911-6028","type":"print"},{"value":"1613-9674","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,17]]},"assertion":[{"value":"4 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}