{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T16:16:05Z","timestamp":1790007365487,"version":"4.0.1"},"reference-count":25,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: Orthodontics is a field that has seen significant advancements in recent years, with technology playing a crucial role in improving diagnosis and treatment planning. The study aimed to implement artificial intelligence to predict the arch width as a preventive measure to avoid future crowding in growing patients or even in adult patients seeking orthodontic treatment as a tool for orthodontic diagnosis. Materials and Methods: Four hundred and fifty intraoral scan (IOS) images were selected from orthodontic patients seeking treatment in private orthodontic centers. Real inter-canine, inter-premolar, and inter-molar widths were measured digitally. Two of the main machine learning models were used: the Python programming language and machine learning algorithms, implementing the data on k-nearest neighbor and linear regression. Results: After the dataset had been implemented on the two ML algorithms, linear regression and k-nearest neighbor, the evaluation metric shows that KNN gives better prediction accuracy than LR does. The resulting accuracy was around 99%. Conclusions: it is possible to leverage machine learning to enhance orthodontic diagnosis and treatment planning by predicting linear dental arch measurements and preventing anterior segment malocclusion.<\/jats:p>","DOI":"10.3390\/medicina59111973","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T03:42:08Z","timestamp":1699501328000},"page":"1973","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study"],"prefix":"10.3390","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1457-8842","authenticated-orcid":false,"given":"Aras Maruf","family":"Rauf","sequence":"first","affiliation":[{"name":"Department of Pedodontics, Orthodontics and Preventive Dentistry, College of Dentistry, University of Sulaimani, Sulaimaniyah 46001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6204-5146","authenticated-orcid":false,"given":"Trefa Mohammed Ali","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Pedodontics, Orthodontics and Preventive Dentistry, College of Dentistry, University of Sulaimani, Sulaimaniyah 46001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1368-6374","authenticated-orcid":false,"given":"Miran Hikmat","family":"Mohammed","sequence":"additional","affiliation":[{"name":"Department of Basic Sciences, College of Dentistry, University of Sulaimani, Sulaimaniyah 46001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zana Qadir","family":"Omer","sequence":"additional","affiliation":[{"name":"Department of Pedodontics, Orthodontics and Preventive Dentistry, College of Dentistry, Hawler Medical University, Erbil 44001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fadil Abdullah","family":"Kareem","sequence":"additional","affiliation":[{"name":"Department of Pedodontics, Orthodontics and Preventive Dentistry, College of Dentistry, University of Sulaimani, Sulaimaniyah 46001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s11604-018-0726-3","article-title":"Deep learning with convolutional neural network in radiology","volume":"36","author":"Yasaka","year":"2018","journal-title":"Jpn. 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