{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:53:49Z","timestamp":1785488029603,"version":"3.56.0"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Children"],"abstract":"<jats:p>Background: The aim of this study was to develop an artificial intelligence (AI)-based decision support model for evaluating the extraction indication of permanent first molars in pediatric patients using panoramic radiographs, and to investigate the potential contribution of this model to the clinical decision-making process. Methods: This retrospective observational study analyzed 1000 panoramic radiographs obtained from children aged 8\u201310 years who attended the Clinics of Batman University Faculty of Dentistry for routine dental examination. Among the radiographs meeting the inclusion criteria, a total of 176 panoramic images were selected based on dental maturation assessment using the Demirjian tooth development staging system. Cases in which the permanent second molar was classified as Demirjian stages E\u2013F were labeled as \u201cextraction indication present\u201d, while the remaining stages were labeled as \u201cextraction indication absent\u201d. A balanced dataset was created, consisting of 88 cases in each group. Image features were extracted using Gabor filters and Histogram of Oriented Gradients (HOG). The selected features were analyzed using a Support Vector Machine (SVM) classifier with a radial basis function (RBF) kernel. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (ROC\u2013AUC). Results: The proposed Gabor\u2013HOG\u2013SVM-based AI model achieved an overall classification accuracy of 77.78% with an AUC value of 0.77 in distinguishing between \u201cextraction indication present\u201d and \u201cextraction indication absent\u201d cases. For the extraction-indicated group, the sensitivity was 0.81 and the F1-score was 0.79, whereas for the non-indicated group, the sensitivity and F1-score were 0.74 and 0.77, respectively. No statistically significant differences were observed between the groups in terms of age or sex distribution (p &gt; 0.05). Conclusions: This study demonstrates that artificial intelligence-based analysis of panoramic radiographic images can provide an objective and reproducible decision support approach for evaluating extraction indications of permanent first molars in pediatric patients. The proposed model should be considered as an adjunctive tool to reduce observer-dependent variability rather than a replacement for clinical judgment, and its clinical applicability should be further validated through multicenter and multi-parametric studies.<\/jats:p>","DOI":"10.3390\/children13020277","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T07:50:30Z","timestamp":1771401030000},"page":"277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence-Based Evaluation of Permanent First Molar Extraction Indications in Children Using Panoramic Radiographs"],"prefix":"10.3390","volume":"13","author":[{"given":"Serap G\u00fcl\u00e7in","family":"\u00c7etin","sequence":"first","affiliation":[{"name":"Faculty of Dentistry, Batman University, 72100 Batman, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00d6mer Faruk","family":"Ertu\u011frul","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Electronic Engineering, Batman University, 72100 Batman, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7293-6461","authenticated-orcid":false,"given":"Nursezen","family":"Kavaso\u011flu","sequence":"additional","affiliation":[{"name":"Faculty of Dentistry, Batman University, 72100 Batman, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veysel","family":"Eratilla","sequence":"additional","affiliation":[{"name":"Faculty of Dentistry, Batman University, 72100 Batman, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"ref_1","first-page":"114","article-title":"Evaluation of common diagnostic errors in panoramic radiographs and interobserver agreement in error identification","volume":"7","author":"Yasa","year":"2025","journal-title":"Med. Rec."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Araidy, S., Batshon, G., and Mirochnik, R. (2025). Artificial intelligence applications in dentistry: A systematic review. Oral, 5.","DOI":"10.3390\/oral5040090"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sensi, F., Lizzi, F., Chincarini, A., Binelli, C., Sartori, L., and Retico, A. (2025). Augmenting, not replacing: Clinicians\u2019 perspectives on AI adoption in healthcare. Appl. Sci., 15.","DOI":"10.3390\/app152312405"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.15311\/selcukdentj.307505","article-title":"\u00c7ocuklarda Daimi Birinci B\u00fcy\u00fck Azi Di\u015f \u00c7ekim Nedenleri Ve Etkileri","volume":"6","author":"Bodrumlu","year":"2019","journal-title":"Selcuk. Dent. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"56","DOI":"10.17214\/gaziaot.348568","article-title":"Daimi birinci b\u00fcy\u00fck az\u0131 di\u015flerinin kontroll\u00fc \u00e7ekimleri: Derleme","volume":"35","author":"Kiraz","year":"2018","journal-title":"Acta Odontol. Turc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1007\/s12663-020-01473-1","article-title":"Prevalence of mandibular third molars according to the Pell & Gregory and Winter classifications","volume":"21","author":"Santos","year":"2022","journal-title":"J. Maxillofac. Oral Surg."},{"key":"ref_7","first-page":"211","article-title":"A new system of dental age assessment","volume":"45","author":"Demirjian","year":"1973","journal-title":"Hum. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1954","DOI":"10.1038\/s41598-021-81449-4","article-title":"Deep learning based prediction of extraction difficulty for mandibular third molars","volume":"11","author":"Yoo","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1177\/0022034520915714","article-title":"Artificial intelligence in dentistry: Chances and challenges","volume":"99","author":"Schwendicke","year":"2020","journal-title":"J. Dent. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jdent.2018.07.015","article-title":"Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm","volume":"77","author":"Lee","year":"2018","journal-title":"J. Dent."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20190107","DOI":"10.1259\/dmfr.20190107","article-title":"The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review","volume":"49","author":"Hung","year":"2020","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kavasoglu, N., Ertugrul, O.F., Kotan, S., Hazar, Y., and Eratilla, V. (2025). Artificial intelligence-assisted wrist radiography analysis in orthodontics: Classification of maturation stage. Appl. Sci., 15.","DOI":"10.3390\/app152111681"},{"key":"ref_13","unstructured":"Faculty of Dental Surgery, Royal College of Surgeons of England (2014). Clinical Excellence Guidelines: Extraction of First Permanent Molars in Children, The Royal College of Surgeons of England. Available online: https:\/\/www.rcseng.ac.uk\/dental-faculties\/fds\/publications-guidelines\/clinical-guidelines\/."},{"key":"ref_14","first-page":"272","article-title":"The timing of extraction of non-restorable first permanent molars: A systematic review","volume":"16","author":"Eichenberger","year":"2015","journal-title":"Eur. J. Paediatr. Dent."},{"key":"ref_15","first-page":"408","article-title":"\u00c7ocuk di\u015fhekimli\u011finde k\u00f6t\u00fc prognozlu daimi birinci molarlar\u0131n \u00e7ekim endikasyonlar\u0131 ve klinik de\u011ferlendirmeleri","volume":"23","author":"Altun","year":"2013","journal-title":"Atat\u00fcrk \u00dcniversitesi Di\u015f Hekim. Fak\u00fcltesi Derg."},{"key":"ref_16","first-page":"87","article-title":"Incidental dental anomalies in pediatric dental patients detected by panoramic radiographs: A retrospective study","volume":"4","author":"Bawazir","year":"2019","journal-title":"Saudi J. Oral Dent. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"73","DOI":"10.70509\/dicledentj.1519676","article-title":"Evaluation of error types seen in digital panoramic radiographs","volume":"25","author":"Kaplan","year":"2024","journal-title":"Dicle Dent. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"114","DOI":"10.5051\/jpis.2018.48.2.114","article-title":"Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm","volume":"48","author":"Lee","year":"2018","journal-title":"J. Periodontal Implant. Sci."},{"key":"ref_19","unstructured":"Demir, P., and Evren, A. (2021). Koruyucu ve Durdurucu Pedodonti ve Ortodonti Uygulamalar\u0131, Livre de Lyon."},{"key":"ref_20","unstructured":"K\u0131rz\u0131o\u011flu, Z., and Ceyhan, D. (2007, January 17\u201321). Erken 6 ya\u015f di\u015fi \u00e7ekim zaman\u0131. Proceedings of the 15 T\u00fcrk Pedodonti Derne\u011fi Kongresi, Antalya, T\u00fcrkiye."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1038\/sj.bdj.2014.1053","article-title":"National clinical guidelines for the extraction of first permanent molars in children","volume":"217","author":"Cobourne","year":"2014","journal-title":"Br. Dent. J."},{"key":"ref_22","unstructured":"Demir, P. (2020). Daimi Birinci Molar Di\u015f \u00c7ekiminden Sonra Spontan Yer Kapanmas\u0131n\u0131 Etkileyen Fakt\u00f6rlerin Radyografik Olarak \u0130ncelenmesi: Bir Retrospektif \u00c7al\u0131\u015fma, T.C. \u0130n\u00f6n\u00fc \u00dcniversitesi Di\u015f Hekimli\u011fi Fak\u00fcltesi."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1093\/ejo\/cjv029","article-title":"Lower first permanent molars: Developing better predictors of spontaneous space closure","volume":"38","author":"Teo","year":"2016","journal-title":"Eur. J. Orthod."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1136\/jramc-2018-001055","article-title":"Artificial intelligence and the radiologist: The future in the Armed Forces Medical Services","volume":"166","author":"Sen","year":"2020","journal-title":"BMJ Mil. Health"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s11604-018-0795-3","article-title":"Technical and clinical overview of deep learning in radiology","volume":"37","author":"Ueda","year":"2019","journal-title":"Jpn. J. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e190058","DOI":"10.1148\/ryai.2019190058","article-title":"Will artificial intelligence replace radiologists?","volume":"1","author":"Langlotz","year":"2019","journal-title":"Radiol. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s41747-018-0061-6","article-title":"Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine","volume":"2","author":"Pesapane","year":"2018","journal-title":"Eur. Radiol. Exp."}],"container-title":["Children"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9067\/13\/2\/277\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T08:10:27Z","timestamp":1771402227000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9067\/13\/2\/277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,17]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["children13020277"],"URL":"https:\/\/doi.org\/10.3390\/children13020277","relation":{},"ISSN":["2227-9067"],"issn-type":[{"value":"2227-9067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,17]]}}}