{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T14:37:04Z","timestamp":1791211024058,"version":"4.1.0"},"reference-count":32,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Orthod Craniofacial Res"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop a Machine Learning\u2010based model to predict growth of Caucasian subjects with untreated Class III malocclusion in the short\u2010 and long\u2010term.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      A longitudinal sample of 144 Caucasian subjects with untreated Class III malocclusion was selected (80% training data; 20% test data). Cephalograms of the subjects of the test group were divided into short\u2010 and long\u2010term observations. Sixteen cephalometric landmarks were digitised in an X\u2010Y Cartesian coordinate system. The trained model was a Graph Neural Network. A one\u2010sample\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test and the Euclidean distances between predicted and observed values were calculated.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the short\u2010term prediction, 16 subjects were examined. On the X\u2010Y axis, the following cephalometric points were statistically significant: SX, PgY, BY, PNSY, NY and SY. Mean Euclidean distance between predicted and actual values revealed high values for the mandibular points Go (2.6\u2009mm), Me (1.9\u2009mm), Gn (1.9\u2009mm), Pg (2.0\u2009mm), and B (2.0\u2009mm). In the long\u2010term prediction, 13 subjects were examined. On the X\u2010Y axis, the following cephalometric points were statistically significant: MeX, GnX, PgX, BX, BY and PtY. Mean Euclidean distance between predicted and actual values revealed high values for the mandibular points Go (3.1\u2009mm), Me (4.3\u2009mm), Gn (4.1\u2009mm), Pg (4.5\u2009mm), and B Point (4.1\u2009mm).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The ML\u2010based prediction model was accurate for the majority of the landmarks. Cephalometric mandibular landmarks (Go, Me, Gn, Pg, and B Points) showed the highest mean Euclidean distances between predicted and observed values, indicating lower prediction accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ocr.70142","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T15:00:26Z","timestamp":1779116426000},"page":"954-962","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning\u2010Based Model for Predicting Short\u2010 and Long\u2010Term Growth in Untreated Class\n                    <scp>III<\/scp>\n                    Malocclusion"],"prefix":"10.1111","volume":"29","author":[{"given":"Maria Denisa","family":"Statie","sequence":"first","affiliation":[{"name":"Department of Experimental and Clinical Medicine Universit\u00e0 degli Studi di Firenze  Firenze Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Nieri","sequence":"additional","affiliation":[{"name":"Department of Experimental and Clinical Medicine Universit\u00e0 degli Studi di Firenze  Firenze Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Rutili","sequence":"additional","affiliation":[{"name":"Department of Experimental and Clinical Medicine Universit\u00e0 degli Studi di Firenze  Firenze Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenzo","family":"Buffoni","sequence":"additional","affiliation":[{"name":"Department of Physics and Astronomy Universit\u00e0 degli Studi di Firenze  Firenze Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenzo","family":"Chicchi","sequence":"additional","affiliation":[{"name":"Department of Physics and Astronomy Universit\u00e0 degli Studi di Firenze  Firenze Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pietro","family":"Auconi","sequence":"additional","affiliation":[{"name":"Private Practice of Orthodontics  Roma Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronica","family":"Giuntini","sequence":"additional","affiliation":[{"name":"Department of Experimental and Clinical Medicine Universit\u00e0 degli Studi di Firenze  Firenze Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James A.","family":"McNamara","sequence":"additional","affiliation":[{"name":"Department of Orthodontics and Pediatric Dentistry The University of Michigan  Ann Arbor Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2072-460X","authenticated-orcid":false,"given":"Lorenzo","family":"Franchi","sequence":"additional","affiliation":[{"name":"Department of Experimental and Clinical Medicine Universit\u00e0 degli Studi di Firenze  Firenze 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