{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T17:56:52Z","timestamp":1793383012661,"version":"4.5.0"},"reference-count":22,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Orthod Craniofacial Res"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To investigate the utility of machine learning (ML) in accurately predicting orthodontic extraction patterns in a heterogeneous population.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>The material of this retrospective study consisted of records of 366 patients treated with orthodontic extractions. The dataset was randomly split into training (70%) and test sets (30%) and was stratified according to race\/ethnicity and gender. Fifty\u2010five cephalometric and demographic input data were used to train and test multiple ML algorithms. The extraction patterns were labelled according to the previous treatment plan. Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms were used to predict the patient's extraction patterns.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The highest class accuracy percentages were obtained for the upper and lower 1st premolars (U\/L4s) (RF: 81.63%, LR: 63.27%, SVM: 63.27%) and upper 1st premolars only (U4s) extraction patterns (RF: 61.11%, LR: 72.22%, SVM: 72.22%). However, all methods revealed low class accuracy rates (&lt;50%) for the upper 1st and lower 2nd premolars (U4\/L5s), upper 2nd and lower 1st premolars (U5\/L4s), and upper and lower 2nd premolars (U\/L5s) extraction patterns. For the overall accuracy, RF yielded the highest percentage with 54.55%, followed by SVM with 52.73% and LR with 49.09%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>All tested supervised ML techniques yielded good accuracy in predicting U\/L4s and U4s extraction patterns. However, they predicted poorly for the U4\/L5s, U5\/L4s, and U\/L5s extraction patterns. Molar relationship, mandibular crowding, and overjet were found to be the most predictive indicators for determining extraction patterns.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ocr.12641","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T08:24:40Z","timestamp":1677486280000},"page":"552-559","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Can we predict orthodontic extraction patterns by using machine learning?"],"prefix":"10.1111","volume":"26","author":[{"given":"Landon","family":"Leavitt","sequence":"first","affiliation":[{"name":"Department of Orthodontics and Oral Facial Genetics Indiana University School of Dentistry  Indianapolis Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Volovic","sequence":"additional","affiliation":[{"name":"Department of Orthodontics and Oral Facial Genetics Indiana University School of Dentistry  Indianapolis Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lily","family":"Steinhauer","sequence":"additional","affiliation":[{"name":"Indiana University School of Dentistry  Indianapolis Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taylor","family":"Mason","sequence":"additional","affiliation":[{"name":"Department of Orthodontics and Oral Facial Genetics Indiana University School of Dentistry  Indianapolis Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Eckert","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Data Science Indiana University School of Medicine  Indianapolis Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey A.","family":"Dean","sequence":"additional","affiliation":[{"name":"Department of Pediatric Dentistry Indiana University School of Dentistry  Indianapolis Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Murat","family":"Dundar","sequence":"additional","affiliation":[{"name":"School of Science, Department of Computer &amp; Information Science Indiana University Purdue University at Indianapolis  Indianapolis Indiana 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  Indianapolis Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40510-021-00361-9"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.25259\/APOS_117_2019"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105513"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2021.03.017"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.2319\/071513-516.1"},{"issue":"159","key":"e_1_2_10_7_1","first-page":"e151","article-title":"An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population","volume":"261","author":"Nino\u2010Sandoval TC","year":"2016","journal-title":"Forensic Sci Int"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2017.10.004"},{"key":"e_1_2_10_9_1","first-page":"12","article-title":"Semantic segmentation of maxillary teeth and palatal Rugae in two\u2010dimensional images","volume":"12","author":"El Bsat AR","year":"2022","journal-title":"Diagnostics (Basel)"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.2319\/012919-59.1"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0269198"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0220242"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12071158"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.2319\/081908-436.1"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.2319\/111608-588.1"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajodo.2015.07.030"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-38439-w"},{"key":"e_1_2_10_18_1","unstructured":"ZaytounML.An Empirical Approach to the Extraction Versus Non\u2010extraction Decision in Orthodontics [Masters Thesis].2019."},{"key":"e_1_2_10_19_1","first-page":"7","article-title":"Machine learning for the diagnosis of orthodontic extractions: a computational analysis using ensemble learning","volume":"7","author":"Suhail Y","year":"2020","journal-title":"Bioengineering (Basel)"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/ocr.12502"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.4041\/kjod.2022.52.2.102"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0000000000005650"},{"key":"e_1_2_10_23_1","first-page":"2825","article-title":"Scikit\u2010learn: machine learning in python","volume":"12","author":"Pedregosa F","year":"2011","journal-title":"J Mach Learn Res"}],"container-title":["Orthodontics &amp; 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