{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T08:38:32Z","timestamp":1762850312058,"version":"build-2065373602"},"reference-count":20,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T00:00:00Z","timestamp":1753401600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Orthod Craniofacial Res"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the accuracy of artificial intelligence (AI) in predicting the effects of headgear (HG) and\/or functional appliance (FA) on the maxillo\u2010mandibular growth in preadolescent patients with skeletal Class II (C\u2010II) malocclusion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      The study included 206 Japanese preadolescent C\u2010II patients treated with HG and\/or FA, with serial lateral cephalograms taken at ages of 8 (T0) and 10 (T1). A single orthodontist with 7\u2009years of experience identified 28 hard\u2010tissue cephalometric landmarks. A Treatment Prediction Graph Convolutional Neural Network (TP\u2010GCNN), integrating a high\u2010resolution network and a graph neural network, was trained and validated using the landmarks'\n                      <jats:italic>x<\/jats:italic>\n                      \u2010 and\n                      <jats:italic>y<\/jats:italic>\n                      \u2010coordinates. Data was split into training, validation and testing sets (ratio of 8:1:1;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009164,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200921 and\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200921). Model performance was assessed using the values of prediction error (PE, excellent, \u2264\u20090.5\u2009mm; very good, 0.5\u20131.0\u2009mm; good, 1.0\u20131.5\u2009mm; acceptable, 1.5\u20132.0\u2009mm; unsatisfactory, &gt;\u20092.0\u2009mm) and the degree of accurate prediction percentage (APP; very high, \u2265\u200990%; high, 70%\u201390%; medium, 50%\u201370%; low, &lt;\u200950%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The mean PE value was 1.45\u2009mm. In terms of PE, all landmarks showed the accuracy above the \u2018acceptable\u2019 category. In terms of APP, \u2018High\u2019 APP was observed at Hinge axis, Pterygoid point, A\u2010point, PNS, ANS, R1, R3 and Articulare. However, \u2018Low\u2019 APP was noted for Pm, Pogonion, B\u2010point and Menton. The remaining landmarks demonstrated \u2018Medium\u2019 APP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates the potential of AI to reliably predict the effects of HG and\/or FA treatment in preadolescent patients with Class II malocclusion.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ocr.70006","type":"journal-article","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T10:40:14Z","timestamp":1753440014000},"page":"955-961","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accuracy of Artificial Intelligence in Predicting the Treatment Effects of Headgear and\/or Functional Appliance on the Maxillo\u2010Mandibular Growth in Preadolescent Patients With Skeletal Class\n                    <scp>II<\/scp>\n                    Malocclusion"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6628-7273","authenticated-orcid":false,"given":"Soichiro","family":"Yamada","sequence":"first","affiliation":[{"name":"Department of Dental Informatics Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences  Okayama Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6471-9661","authenticated-orcid":false,"given":"Hee Ji","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of Ulsan College of Medicine, Asan Medical Center  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7975-2159","authenticated-orcid":false,"given":"Youna","family":"Huh","sequence":"additional","affiliation":[{"name":"Arizona School of Dentistry &amp; Oral Health, A.T. Still University  Mesa Arizona USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7129-5115","authenticated-orcid":false,"given":"Yoshinobu","family":"Yanagi","sequence":"additional","affiliation":[{"name":"Department of Oral Radiology Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences  Okayama Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3134-6878","authenticated-orcid":false,"given":"Jae Hyun","family":"Park","sequence":"additional","affiliation":[{"name":"Postgraduate Orthodontic Program, Arizona School of Dentistry &amp; Oral Health A.T. Still University  Mesa Arizona USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0972-0488","authenticated-orcid":false,"given":"Kiyoshi","family":"Tai","sequence":"additional","affiliation":[{"name":"Postgraduate Orthodontic Program, Arizona School of Dentistry &amp; Oral Health A.T. Still University  Mesa Arizona USA"},{"name":"Private Practice of Orthodontics  Okayama Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-2217","authenticated-orcid":false,"given":"Namkug","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Brain Korea 21 Project University of Ulsan College of Medicine, Asan Medical Center  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6586-9503","authenticated-orcid":false,"given":"Seung\u2010Hak","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Orthodontics School of Dentistry, Dental Research Institute, Seoul National University  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/ejo\/cju071"},{"issue":"3","key":"e_1_2_10_3_1","first-page":"177","article-title":"Components of Class II Malocclusion in Children 8\u201310\u2009Years of Age","volume":"51","author":"McNamara J. 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