{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T03:07:25Z","timestamp":1793502445804,"version":"4.5.0"},"reference-count":40,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000072","name":"National Institute of Dental and Craniofacial Research","doi-asserted-by":"publisher","award":["NIH: R01DE024450"],"award-info":[{"award-number":["NIH: R01DE024450"]}],"id":[{"id":"10.13039\/100000072","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":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics. The increasing availability of data and the era of big data enable the use of artificial intelligence to guide clinicians' diagnoses. This study aims to test the capacity of different machine learning (ML) models to predict whether orthognathic surgery or orthodontics treatment is required, using soft and hard tissue cephalometric values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A total of 920 lateral radiographs from patients previously treated with either conventional orthodontics or in combination with orthognathic surgery were used, comprising\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009558 Class II and\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009362 Class III patients, respectively. Thirty\u2010two measures were obtained from each cephalogram at the initial appointment. The subjects were randomly divided into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009552), validation (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009183), and test (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009185) datasets, both as an entire sample and divided into Class II and Class III sub\u2010groups. The extracted data were evaluated using 10 machine learning models and by a four\u2010expert panel consisting of orthodontists (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092) and surgeons (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The combined prediction of 10 models showed top\u2010ranked performance in the testing dataset for accuracy, F1\u2010score, and AUC (entire sample: 0.707, 0.706, 0.791; Class II: 0.759, 0.758, 0.824; Class III: 0.822, 0.807, 0.89).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed combined 10 ML approach model accurately predicted the need for orthognathic surgery, showing better performance in Class III patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ocr.12805","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T02:25:28Z","timestamp":1715135128000},"page":"785-794","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Artificial intelligence as a prediction tool for orthognathic surgery assessment"],"prefix":"10.1111","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0422-5149","authenticated-orcid":false,"given":"Pedro Henrique Jos\u00e9","family":"de Oliveira","sequence":"first","affiliation":[{"name":"Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry S\u00e3o Paulo State University (Unesp)  Araraquara S\u00e3o Paulo 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Dentistry S\u00e3o Paulo State University (Unesp)  Araraquara S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3355-0001","authenticated-orcid":false,"given":"Ary","family":"Santos\u2010Pinto","sequence":"additional","affiliation":[{"name":"Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry S\u00e3o Paulo State University (Unesp)  Araraquara S\u00e3o Paulo Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8656-6010","authenticated-orcid":false,"given":"Luiz Gonzaga","family":"Gandini\u00a0Junior","sequence":"additional","affiliation":[{"name":"Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry S\u00e3o Paulo State University (Unesp)  Araraquara S\u00e3o Paulo 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