{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T19:00:46Z","timestamp":1788894046980,"version":"build-2803163510"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T00:00:00Z","timestamp":1767484800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T00:00:00Z","timestamp":1767484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Oral Radiol"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s11282-025-00887-2","type":"journal-article","created":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T03:12:45Z","timestamp":1767496365000},"page":"552-557","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Impact of image processing techniques on deep learning-based classification accuracy of cervical vertebral maturation"],"prefix":"10.1007","volume":"42","author":[{"given":"Naoki","family":"Yamada","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yukihiro","family":"Iida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wakako","family":"Tome","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akitoshi","family":"Katsumata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noriyuki","family":"Kitai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,4]]},"reference":[{"key":"887_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102444","author":"X Chen","year":"2022","unstructured":"Chen X, Wang X, Zhang K, Fung KM, Thai TC, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal. 2022. https:\/\/doi.org\/10.1016\/j.media.2022.102444.","journal-title":"Med Image Anal"},{"key":"887_CR2","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1053\/j.akdh.2022.11.003","volume":"30","author":"H Zhang","year":"2023","unstructured":"Zhang H, Botler M, Kooman JP. Deep learning for image analysis in kidney care. Adv Kidney Dis Health. 2023;30:25\u201332. https:\/\/doi.org\/10.1053\/j.akdh.2022.11.003.","journal-title":"Adv Kidney Dis Health"},{"key":"887_CR3","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.3233\/XST-210955","volume":"29","author":"W Tan","year":"2021","unstructured":"Tan W, Zhou L, Li X, Yang X, Chen Y, Yang J. Automated vessel segmentation in lung CT and CTA images via deep neural networks. J Xray Sci Technol. 2021;29:1123\u201337. https:\/\/doi.org\/10.3233\/XST-210955.","journal-title":"J Xray Sci Technol"},{"key":"887_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104730","author":"A Ben Hamida","year":"2021","unstructured":"Ben Hamida A, Devanne M, Weber J, Truntzer C, Derang\u00e8re V, Ghiringhelli F, Forestier G, Wemmert C. Deep learning for colon cancer histopathological images analysis. Comput Biol Med. 2021. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104730.","journal-title":"Comput Biol Med"},{"key":"887_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105420","author":"M Gherardini","year":"2020","unstructured":"Gherardini M, Mazomenos E, Menciassi A, Stoyanov D. Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets. Comput Methods Programs Biomed. 2020. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105420.","journal-title":"Comput Methods Programs Biomed"},{"key":"887_CR6","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s11282-021-00538-2","volume":"38","author":"M Mori","year":"2022","unstructured":"Mori M, Ariji Y, Fukuda M, Kitano T, Funakoshi T, Nishiyama W, Kohinata K, Iida Y, Ariji E, Katsumata A. Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine. Oral Radiol. 2022;38:147\u201354. https:\/\/doi.org\/10.1007\/s11282-021-00538-2.","journal-title":"Oral Radiol"},{"key":"887_CR7","doi-asserted-by":"publisher","first-page":"53","DOI":"10.11242\/dentalradiology.60.53","volume":"60","author":"K Kitano","year":"2021","unstructured":"Kitano K, Mori M, Nishiyama W, Iida Y, Fujita H, Katsumata A. Classification of intraoral X-ray images using artificial intelligence. Dent Radiol. 2021;60:53\u20137. https:\/\/doi.org\/10.11242\/dentalradiology.60.53.","journal-title":"Dent Radiol"},{"key":"887_CR8","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.oooo.2020.04.813","volume":"130","author":"C Kuwada","year":"2020","unstructured":"Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, Ariji E. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130:464\u20139. https:\/\/doi.org\/10.1016\/j.oooo.2020.04.813.","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"887_CR9","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s11282-019-00418-w","volume":"37","author":"C Muramatsu","year":"2021","unstructured":"Muramatsu C, Morishita T, Takahashi R, Hayashi T, Nishiyama W, Ariji Y, Zhou X, Hara T, Katsumata A, Ariji E, Fujita H. Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data. Oral Radiol. 2021;37:13\u20139. https:\/\/doi.org\/10.1007\/s11282-019-00418-w.","journal-title":"Oral Radiol"},{"key":"887_CR10","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/3625386","author":"M Prados-Privado","year":"2021","unstructured":"Prados-Privado M, Garc\u00eda Villal\u00f3n J, Bl\u00e1zquez Torres A, Mart\u00ednez-Mart\u00ednez CH, Ivorra C. A convolutional neural network for automatic tooth numbering in panoramic images. Biomed Res Int. 2021. https:\/\/doi.org\/10.1155\/2021\/3625386.","journal-title":"Biomed Res Int"},{"key":"887_CR11","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.oooo.2020.04.005","volume":"130","author":"M Fukuda","year":"2020","unstructured":"Fukuda M, Ariji Y, Kise Y, Nozawa M, Kuwada C, Funakoshi T, Muramatsu C, Fujita H, Katsumata A, Ariji E. Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130:336\u201343. https:\/\/doi.org\/10.1016\/j.oooo.2020.04.005.","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"key":"887_CR12","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compbiomed.2016.11.003","volume":"80","author":"Y Miki","year":"2017","unstructured":"Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, Fujita H. Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med. 2017;80:24\u20139. https:\/\/doi.org\/10.1016\/j.compbiomed.2016.11.003.","journal-title":"Comput Biol Med"},{"key":"887_CR13","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.ajodo.2021.02.013","volume":"160","author":"H Mohammad-Rahimi","year":"2021","unstructured":"Mohammad-Rahimi H, Nadimi M, Rohban MH, Shamsoddin E, Lee VY, Motamedian SR. Machine learning and orthodontics, current trends and the future opportunities: a scoping review. Am J Orthod Dentofac Orthop. 2021;160:170\u201392. https:\/\/doi.org\/10.1016\/j.ajodo.2021.02.013.","journal-title":"Am J Orthod Dentofac Orthop"},{"key":"887_CR14","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1088\/1757-899X\/300\/1\/012029","volume":"72","author":"T Baccetti","year":"2002","unstructured":"Baccetti T, Franchi L, McNamara Jr JA. An improved version of the cervical vertebral maturation (CVM) method for the assessment of mandibular growth. Angle Orthod. 2002;72:316\u201323. https:\/\/doi.org\/10.1088\/1757-899X\/300\/1\/012029.","journal-title":"Angle Orthod"},{"key":"887_CR15","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1053\/j.sodo.2005.04.005","volume":"11","author":"T Baccetti","year":"2005","unstructured":"Baccetti T, Franchi L, McNamara JA. The cervical vertebral maturation (CVM) method for the assessment of optimal treatment timing in dentofacial orthopedics. Semin Orthod. 2005;11:119\u201329. https:\/\/doi.org\/10.1053\/j.sodo.2005.04.005.","journal-title":"Semin Orthod"},{"key":"887_CR16","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20190441","author":"H Amasya","year":"2020","unstructured":"Amasya H, Yildirim D, Aydogan T, Kemaloglu N, Orhan K. Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models. Dentomaxillofac Radiol. 2020. https:\/\/doi.org\/10.1259\/dmfr.20190441.","journal-title":"Dentomaxillofac Radiol"},{"key":"887_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10163591","author":"H Seo","year":"2021","unstructured":"Seo H, Hwang J, Jeong T, Shin J. Comparison of deep learning models for cervical vertebral maturation stage cassification on lateral cephalometric radiographs. J Clin Med. 2021. https:\/\/doi.org\/10.3390\/jcm10163591.","journal-title":"J Clin Med"},{"key":"887_CR18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0269198","author":"SF Atici","year":"2022","unstructured":"Atici SF, Ansari R, Allareddy V, Suhaym O, Cetin AE, Elnagar MH. Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters. PLoS One. 2022. https:\/\/doi.org\/10.1371\/journal.pone.0269198.","journal-title":"PLoS One"},{"key":"887_CR19","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10225400","author":"EG Kim","year":"2021","unstructured":"Kim EG, Oh IS, So JE, Kang J, Le VNT, Tak MK, Lee DW. Estimating cervical vertebral maturation with a lateral cephalogram using the convolutional neural network. J Clin Med. 2021. https:\/\/doi.org\/10.3390\/jcm10225400.","journal-title":"J Clin Med"},{"key":"887_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11122200","author":"J Zhou","year":"2021","unstructured":"Zhou J, Zhou H, Pu L, Gao Y, Tang Z, Yang Y, You M, Yang Z, Lai W, Long H. Development of an artificial intelligence system for the automatic evaluation of cervical vertebral maturation status. Diagnostics. 2021. https:\/\/doi.org\/10.3390\/diagnostics11122200.","journal-title":"Diagnostics"},{"key":"887_CR21","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20220070","author":"H Li","year":"2022","unstructured":"Li H, Chen Y, Wang Q, Gong X, Lei Y, Tian J, Gao X. Convolutional neural network-based automatic cervical vertebral maturation classification method. Dentomaxillofac Radiol. 2022. https:\/\/doi.org\/10.1259\/dmfr.20220070.","journal-title":"Dentomaxillofac Radiol"},{"key":"887_CR22","doi-asserted-by":"publisher","first-page":"112","DOI":"10.4041\/kjod.2022.52.2.112","volume":"52","author":"H Mohammad-Rahimi","year":"2022","unstructured":"Mohammad-Rahimi H, Motamadian SR, Nadimi M, Hassanzadeh-Samani S, Minabi MAS, Mahmoudinia E, Lee VY, Rohban MH. Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study. Korean J Orthod. 2022;52:112\u201322. https:\/\/doi.org\/10.4041\/kjod.2022.52.2.112.","journal-title":"Korean J Orthod"},{"key":"887_CR23","doi-asserted-by":"publisher","DOI":"10.1111\/ocr.12644","author":"SF Atici","year":"2023","unstructured":"Atici SF, Ansari R, Allareddy V, Suhaym O, Cetin AE, Elnagar MH. AggregateNet:: a deep learning model for automated classification of cervical vertebrae maturation stages. Orthod Craniofac Res. 2023. https:\/\/doi.org\/10.1111\/ocr.12644.","journal-title":"Orthod Craniofac Res"},{"key":"887_CR24","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s11282-023-00678-7","volume":"39","author":"G Akay","year":"2023","unstructured":"Akay G, Akcayol MA, \u00d6zdem K, G\u00fcng\u00f6r K. Deep convolutional neural network-the evaluation of cervical vertebrae maturation. Oral Radiol. 2023;39:629\u201338. https:\/\/doi.org\/10.1007\/s11282-023-00678-7.","journal-title":"Oral Radiol"},{"key":"887_CR25","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s11282-022-00634-x","volume":"39","author":"K Kohinata","year":"2023","unstructured":"Kohinata K, Kitano T, Nishiyama W, Mori M, Iida Y, Fujita H, Katsumata A. Deep learning for preliminary profiling of panoramic images. Oral Radiol. 2023;39:275\u201381. https:\/\/doi.org\/10.1007\/s11282-022-00634-x.","journal-title":"Oral Radiol"},{"key":"887_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104129","author":"M Salvi","year":"2021","unstructured":"Salvi M, Acharya UR, Molinari F, Meiburger KM. The impact of pre- and post-image processing techniques on deep learning frameworks: a comprehensive review for digital pathology image analysis. Comput Biol Med. 2021. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104129.","journal-title":"Comput Biol Med"}],"container-title":["Oral Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11282-025-00887-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11282-025-00887-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11282-025-00887-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T17:36:19Z","timestamp":1782236179000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11282-025-00887-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,4]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["887"],"URL":"https:\/\/doi.org\/10.1007\/s11282-025-00887-2","relation":{},"ISSN":["0911-6028","1613-9674"],"issn-type":[{"value":"0911-6028","type":"print"},{"value":"1613-9674","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,4]]},"assertion":[{"value":"18 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 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":"The authors declare no conflict of interest related to this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was approved by the Ethics Committee of Asahi University Graduate School of Dentistry (No. 31029).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Opt-out procedure was performed as substitute for informed consent because this study is a retrospective study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}