{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T14:37:10Z","timestamp":1791211030227,"version":"4.1.0"},"reference-count":47,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T00:00:00Z","timestamp":1692662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and Y-axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test seven ML algorithms. For all ML methods tested, the mean absolute errors (MAEs) for the 2-year prediction ranged from 2.78 to 5.40 mm and 0.88 to 1.48 degrees, respectively. For the 4-year prediction, MAEs of mandibular length and Y-axis ranged from 3.21 to 4.00 mm and 1.19 to 5.12 degrees, respectively. The most predictive factors for post-pubertal mandibular length were mandibular length at previous timepoints, age, sagittal positions of the maxillary and mandibular skeletal bases, mandibular plane angle, and anterior and posterior face heights. The most predictive factors for post-pubertal Y-axis were Y-axis at previous timepoints, mandibular plane angle, and sagittal positions of the maxillary and mandibular skeletal bases. ML methods were identified as capable of predicting mandibular length within 3 mm and Y-axis within 1 degree. Compared to each other, all of the ML algorithms were similarly accurate, with the exception of multilayer perceptron regressor.<\/jats:p>","DOI":"10.3390\/diagnostics13172729","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T09:10:56Z","timestamp":1692695456000},"page":"2729","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Short- and Long-Term Prediction of the Post-Pubertal Mandibular Length and Y-Axis in Females Utilizing Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Matthew","family":"Parrish","sequence":"first","affiliation":[{"name":"Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ella","family":"O\u2019Connell","sequence":"additional","affiliation":[{"name":"Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7798-7155","authenticated-orcid":false,"given":"George","family":"Eckert","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jay","family":"Hughes","sequence":"additional","affiliation":[{"name":"Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarkhan","family":"Badirli","sequence":"additional","affiliation":[{"name":"Eli Lilly and Company, Indianapolis, IN 46225, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9052-7700","authenticated-orcid":false,"given":"Hakan","family":"Turkkahraman","sequence":"additional","affiliation":[{"name":"Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.coms.2020.01.007","article-title":"Craniofacial Growth: Current Theories and Influence on Management","volume":"32","author":"Manlove","year":"2020","journal-title":"Oral Maxillofac. 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