{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T14:37:03Z","timestamp":1791211023551,"version":"4.1.0"},"reference-count":22,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Orthod Craniofacial Res"],"published-print":{"date-parts":[[2024,8]]},"abstract":"Abstract<\/jats:title>\n \n Objective<\/jats:title>\n To investigate the accuracy of artificial intelligence\u2010assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat\u2010cephs).<\/jats:p>\n <\/jats:sec>\n \n Materials and Methods<\/jats:title>\n A total of 198 Japanese preadolescent children, who had skeletal Class I malocclusion and whose Lat\u2010cephs were available at age 8\u2009years (T0) and 10\u2009years (T1), were allocated into the training, validation, and test phases (n\u2009=\u2009161, n\u2009=\u200917, n\u2009=\u200920). Orthodontists and the CNN model identified 28 hard\u2010tissue landmarks (HTL) and 19 soft\u2010tissue landmarks (STL). The mean prediction error values were defined as \u2018excellent,\u2019 \u2018very good,\u2019 \u2018good,\u2019 \u2018acceptable,\u2019 and \u2018unsatisfactory\u2019 (criteria: 0.5\u2009mm, 1.0\u2009mm, 1.5\u2009mm, and 2.0\u2009mm, respectively). The degree of accurate prediction percentage (APP) was defined as \u2018very high,\u2019 \u2018high,\u2019 \u2018medium,\u2019 and \u2018low\u2019 (criteria: 90%, 70%, and 50%, respectively) according to the percentage of subjects that showed the error range within 1.5\u2009mm.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n All HTLs showed acceptable\u2010to\u2010excellent mean PE values, while the STLs Pog\u2019, Gn\u2019, and Me\u2019 showed unsatisfactory values, and the rest showed good\u2010to\u2010acceptable values. Regarding the degree of APP, HTLs Ba, ramus posterior, Pm, Pog, B\u2010point, Me, and mandibular first molar root apex exhibited low APPs. The STLs labrale superius, lower embrasure, lower lip, point of lower profile, B\u2032, Pog,\u2019 Gn\u2019 and Me\u2019 also exhibited low APPs. The remainder of HTLs and STLs showed medium\u2010to\u2010very high APPs.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Despite the possibility of using the CNN model to predict growth, further studies are needed to improve the prediction accuracy in HTLs and STLs of the chin area.<\/jats:p>\n <\/jats:sec>","DOI":"10.1111\/ocr.12764","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T01:40:55Z","timestamp":1707270055000},"page":"535-543","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Accuracy of artificial intelligence\u2010assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2\u2010year growth interval"],"prefix":"10.1111","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6013-0364","authenticated-orcid":false,"given":"A.","family":"Larkin","sequence":"first","affiliation":[{"name":"Postgraduate Orthodontic Program, Arizona School of Dentistry & Oral Health A.T. Still University Mesa Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1672-1737","authenticated-orcid":false,"given":"J.\u2010S.","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-2217","authenticated-orcid":false,"given":"N.","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Convergence Medicine University of Ulsan, College of Medicine, Asan Medical Center Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6586-9503","authenticated-orcid":false,"given":"S.\u2010H.","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, School of Dentistry, Dental Research Institute Seoul National University Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6628-7273","authenticated-orcid":false,"given":"S.","family":"Yamada","sequence":"additional","affiliation":[{"name":"Department of Dental Informatics Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences Okayama Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5525-3869","authenticated-orcid":false,"given":"K.","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0972-0488","authenticated-orcid":false,"given":"K.","family":"Tai","sequence":"additional","affiliation":[{"name":"Postgraduate Orthodontic Program, Arizona School of Dentistry & Oral Health A.T. Still University Mesa Arizona USA"},{"name":"Private Practice of Orthodontics Okayama Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7129-5115","authenticated-orcid":false,"given":"Y.","family":"Yanagi","sequence":"additional","affiliation":[{"name":"Department of Dental Informatics Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences Okayama Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3134-6878","authenticated-orcid":false,"given":"J. H.","family":"Park","sequence":"additional","affiliation":[{"name":"Postgraduate Orthodontic Program, Arizona School of Dentistry & Oral Health A.T. Still University Mesa Arizona USA"},{"name":"Graduate School of Dentistry Kyung Hee University Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/0002-9416(48)90015-3"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0002-9416(84)90352-X"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40510-021-00361-9"},{"issue":"1","key":"e_1_2_10_5_1","first-page":"1294","article-title":"Malocclusion classification on 3D cone\u2010beam CT craniofacial images using multi\u2010channel deep learning models","volume":"2020","author":"Kim I","year":"2020","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/0022034520901715"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/ocr.12493"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.4041\/kjod.2022.52.1.3"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.4041\/kjod21.248"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/ejo\/cjab054"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2021.11.011"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/ocr.12683"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2005.10.026"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare9111545"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40510-019-0295-8"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejwf.2023.02.003"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-023-02734-4"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13091553"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.2319\/031723-181.1"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.sodo.2021.05.005"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.2319\/022019-129.1"},{"key":"e_1_2_10_22_1","first-page":"2847","article-title":"On the expressive power of deep neural networks","volume":"70","author":"Raghu M","year":"2017","journal-title":"Proc Mach Learn Res"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs11091051"}],"container-title":["Orthodontics & Craniofacial Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/ocr.12764","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T11:17:40Z","timestamp":1720005460000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/ocr.12764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,6]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["10.1111\/ocr.12764"],"URL":"https:\/\/doi.org\/10.1111\/ocr.12764","archive":["Portico"],"relation":{},"ISSN":["1601-6335","1601-6343"],"issn-type":[{"value":"1601-6335","type":"print"},{"value":"1601-6343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,6]]},"assertion":[{"value":"2024-01-23","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}