{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T16:31:01Z","timestamp":1787070661191,"version":"build-2736575974"},"reference-count":37,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"5","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["J Int Soc Prev Community Dent"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>\n                    A\n                    <jats:sc>bstract<\/jats:sc>\n                  <\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims:<\/jats:title>\n                    <jats:p>The sagittal skeletal correlation involving the maxilla and mandible is commonly assessed utilizing the A point, Nasion, B point (ANB) angle and Wits appraisal. Recently, ethnicity-specific diagnostic values have been proposed to improve accuracy. This study investigates whether machine learning (ML) models can classify Arab orthodontic cases identified with skeletal class I or class III determined only by standard skeletal measurement indicators, without relying on individualized equations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>Lateral cephalograms from 422 Arab orthodontic patients were analyzed in this study. Five supervised ML algorithms\u2014linear discriminant analysis, support vector machine, K-nearest neighbor, random forest, and Classification and Regression tree\u2014were developed and evaluated utilizing a 10-fold resampling technique. This study compared full-feature models with those using limited parameter sets, such as the Wits index and Sella\u2013Nasion\u2013B point angle measurement. Additionally, regression models were constructed to predict ANB angle using Wits, age, and gender.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Full-feature models achieved up to 97% accuracy, whereas reduced models maintained high performance (up to 91%) using Wits, S-N-B, and S-N-Pg angles. A marked positive association (\n                      <jats:italic toggle=\"yes\">r<\/jats:italic>\n                      = 0.55,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.01) was found shared by Wits and ANB in class III patients. The best regression model (\n                      <jats:italic toggle=\"yes\">R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.57) predicted ANB using the formula: ANB = 4.37 + (0.47 \u00d7 Wits) + (1.07 \u00d7 gender) + (0.04 \u00d7 age).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>ML models can effectively classify skeletal classes I and III malocclusions in Arab subjects using basic cephalometric data, offering a reliable alternative to individualized assessment tools.<\/jats:p>\n                  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Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nezar","family":"Watted","sequence":"additional","affiliation":[{"name":"Center for Dentistry Research and Aesthetics, Jatt, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuad A.","family":"Iraqi","sequence":"additional","affiliation":[{"name":"Department of Clinical Microbiology and Immunology, Gray Faculty of Medicine and Health Sciences, Tel Aviv University, Tel Aviv, Israel"},{"name":"Department of Orthodontics, University Hospital of Regensburg, University of Regensburg, Regensburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"CIT0001-20251030","first-page":"350","article-title":"Classification of malocclusion","volume":"41","author":"Angle","year":"1899","journal-title":"Dent 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