{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T09:11:57Z","timestamp":1777972317943,"version":"3.51.4"},"reference-count":35,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100005648","name":"American Association of Orthodontists Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005648","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Orthod Craniofacial Res"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The aim of this study was to develop and apply an artificial intelligence (AI) algorithm to aid the clinical decision\u2010making process for managing mandibular retained second deciduous molars (SDM) with no permanent successors using machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study consisted of patients who were diagnosed with at least one congenitally missing (agenic) mandibular permanent second premolar with a retained SDM. Pretreatment clinical records from each patient were collected and three sets of input features (radiographic, photographic and clinical) were used. The sample was divided into three groups, each representing a distinct treatment decision: (1) extraction of the SDM with space closure; (2) extraction of the SDM with space maintenance; and (3) retention of the SDM. The treatment decisions were based on majority treatment determination by three experienced clinicians. Four machine learning models were built and evaluated: Multinomial Logistic Regression, Multilayer Perceptron, Decision Tree and Random Forest classifier.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Random Forest classifier showed the highest accuracy in treatment planning while Decision Tree showed the lowest accuracy. Features such as patient preference for restoration, amount of mandibular arch crowding and ankylosis were the strongest predictors, having the greatest influence on treatment decision accuracy in the Random Forest classifier model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The Random Forest classifier demonstrated the highest accuracy in aiding the clinical decision\u2010making process for managing retained SDM with no permanent successors. Key factors influencing treatment decision accuracy included patient preference for restoration, mandibular arch crowding and ankylosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ocr.70100","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T11:06:55Z","timestamp":1768561615000},"page":"576-585","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence\u2010Assisted Clinical Decision Model for Managing Retained Second Deciduous Molars With No Permanent Successors"],"prefix":"10.1111","volume":"29","author":[{"given":"Ozge","family":"Colak","sequence":"first","affiliation":[{"name":"Private Practice  Katy Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William","family":"Tanberg","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health and Health Professions State University of New York at Buffalo  Buffalo New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2332-9250","authenticated-orcid":false,"given":"Mohammed H.","family":"Elnagar","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, College of Dentistry University of Illinois Chicago  Chicago Illinois USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6857-747X","authenticated-orcid":false,"given":"Thikriat","family":"Al\u2010Jewair","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, School of Dental Medicine State University of New York at Buffalo  Buffalo New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.4103\/0970\u20104388.84677"},{"issue":"1","key":"e_1_2_13_3_1","first-page":"23","article-title":"Tooth Agenesis in Patients Referred to an Irish Tertiary Care Clinic for the Developmental Dental Disorders","volume":"56","author":"Hashem A. 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