{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:18:34Z","timestamp":1787015914708,"version":"build-2736575974"},"reference-count":37,"publisher":"Wiley","issue":"S2","license":[{"start":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T00:00:00Z","timestamp":1622764800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["22792048"],"award-info":[{"award-number":["22792048"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["25862008"],"award-info":[{"award-number":["25862008"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Orthod Craniofacial Res"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To determine whether AI systems that recognize cephalometric landmarks can apply to various patient groups and to examine the patient\u2010related factors associated with identification errors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting and sample population<\/jats:title>\n                    <jats:p>The present retrospective cohort study analysed digital lateral cephalograms obtained from 1785 Japanese orthodontic patients. Patients were categorized into eight subgroups according to dental age, cleft lip and\/or palate, orthodontic appliance use and overjet.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>An AI system that automatically recognizes anatomic landmarks on lateral cephalograms was used. Thirty cephalograms in each subgroup were randomly selected and used to test the system's performance. The remaining cephalograms were used for system learning. The success rates in landmark recognition were evaluated using confidence ellipses with \u03b1\u00a0=\u00a00.99 for each landmark. The selection of test samples, learning of the system and evaluation of the system were repeated five times for each subgroup. The mean success rate and identification error were calculated. Factors associated with identification errors were examined using a multiple linear regression model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The success rate and error varied among subgroups, ranging from 85% to 91% and 1.32\u00a0mm to 1.50\u00a0mm, respectively. Cleft lip and\/or palate was found to be a factor associated with greater identification errors, whereas dental age, orthodontic appliances and overjet were not significant factors (all,\n                      <jats:italic>P\u00a0&lt;<\/jats:italic>\n                      \u00a0.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Artificial intelligence systems that recognize cephalometric landmarks could be applied to various patient groups. Patient\u2010oriented errors were found in patients with cleft lip and\/or palate.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ocr.12501","type":"journal-article","created":{"date-parts":[[2021,5,22]],"date-time":"2021-05-22T09:41:36Z","timestamp":1621676496000},"page":"43-52","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Clinical applicability of automated cephalometric landmark identification: Part I\u2014Patient\u2010related identification errors"],"prefix":"10.1111","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3390-2900","authenticated-orcid":false,"given":"Chihiro","family":"Tanikawa","sequence":"first","affiliation":[{"name":"Graduate School of Dentistry Osaka University  Suita Japan"},{"name":"Center for Advanced Medical Engineering and Informatics Osaka University  Suita Japan"},{"name":"Institute for Datability Science Osaka University  Suita Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chonho","family":"Lee","sequence":"additional","affiliation":[{"name":"Cybermedia Center Osaka University  Suita Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaeyoen","family":"Lim","sequence":"additional","affiliation":[{"name":"Graduate School of Dentistry Osaka University  Suita Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayaka","family":"Oka","sequence":"additional","affiliation":[{"name":"Graduate School of Dentistry Osaka University  Suita Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4419-9643","authenticated-orcid":false,"given":"Takashi","family":"Yamashiro","sequence":"additional","affiliation":[{"name":"Graduate School of Dentistry Osaka University  Suita Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"e_1_2_9_2_1","first-page":"333","article-title":"Automatic landmarking of cephalograms by cellular neural networks","volume":"3581","author":"Giordano D","year":"2005","journal-title":"Lect Notes Artif Int"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"GiordanoD LeonardiR MaioranaF SpampinatoC.Cellular neural networks and dynamic enhancement for cephalometric landmarks detection.Artificial Intelligence and Soft Computing \u2010 ICAISC 2006 Proceedings.2006;4029:768\u2010777.","DOI":"10.1007\/11785231_80"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1155\/2009\/717102"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.2319\/092908-508R.1"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1525-1594.2010.00994.x"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.2319\/092909-474.1"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/ejo\/cjp099"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.4.1.014501"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.2319\/022019-129.1"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0000000000004901"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.2319\/022019-127.1"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.2319\/022019-129.1"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00056-019-00203-8"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.2319\/081908-436.1"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.2319\/081908-439.1"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.2319\/111608-588.1"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2015.07.030"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.4258\/hir.2018.24.1.22"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0000000000005650"},{"key":"e_1_2_9_21_1","unstructured":"KajiwaraT TanikawaC ShimizuY ChuC YamashiroT NagaharaH.Using natural language processing to develop an automated orthodontic diagnostic system.2019:arXiv:1905.13601.https:\/\/ui.adsabs.harvard.edu\/abs\/2019arXiv190513601K. 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