{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T19:19:50Z","timestamp":1788808790257,"version":"build-2803163510"},"reference-count":37,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Orthod Craniofacial Res"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The study aims to evaluate the effectiveness of deep learning algorithms in skeletal age estimation by comparing the diagnostic reliability of five different hand\u2010wrist maturation (HWM) assessment methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A total of 6572 hand\u2010wrist radiographs from orthodontic patients aged 8\u201316\u2009years were retrospectively analysed. Radiographs were categorised into five groups based on HWM classification methods: (I) Bj\u00f6rk's nine\u2010stage, (II) H\u00e4gg and Taranger's five\u2010stage, (III) Chapman's four\u2010stage, (IV) three\u2010stage hook of hamate ossification based and (V) simplified three\u2010stage Bj\u00f6rk's classification based. YOLOv8x\u2010based deep learning models were trained separately for each group. The dataset was split into training, validation and test subsets. Performance was evaluated using accuracy, precision, recall, F1 score and AUC metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The YOLOv8x\u2010cls model demonstrated high classification performance across all five groups. Group IV and Group II achieved the highest accuracy and F1 scores, with average F1 values of 0.99 and 0.96, respectively. Group III and Group V also showed strong performance (F1\u2009=\u20090.93 and 0.92). In Group I, slightly lower classification performance was observed in the S\u2010H2 and MP3\u2010Cap stages (F1\u2009=\u20090.72\u20130.74), which correspond to the pubertal growth peak, while early and late skeletal maturation stages were classified with high accuracy and F1 scores. ROC curve analysis further supported these findings, with AUC values for MP3\u2010Cap and S\u2010H2 recorded as 0.70 and 0.75, respectively, whereas higher AUC values were achieved in most other stages across all groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Deep learning models proved effective in evaluating skeletal maturation across five different HWM methods. Particularly high performance was observed in anatomically distinct regions such as the MP3, adductor sesamoid and hamate bone, which can be reliably identified by general dentists, enabling earlier referrals and timely orthodontic interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ocr.70008","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T11:43:57Z","timestamp":1753357437000},"page":"943-954","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Deep Learning Based Evaluation of Skeletal Maturation: A Comparative Analysis of Five Hand\u2010Wrist Methods"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7842-2081","authenticated-orcid":false,"given":"Serhat","family":"Tenta\u015f","sequence":"first","affiliation":[{"name":"Department of Orthodontics Faculty of Dentistry, \u0130n\u00f6n\u00fc University  Battalgazi, Malatya T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9733-9777","authenticated-orcid":false,"given":"Samet","family":"\u00d6zden","sequence":"additional","affiliation":[{"name":"Department of Orthodontics Faculty of Dentistry, \u0130n\u00f6n\u00fc University  Battalgazi, Malatya T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0889-5406(98)70272-0"},{"issue":"3","key":"e_1_2_12_3_1","first-page":"181","article-title":"Chronological Versus Skeletal Age, an Evaluation of Craniofacial Growth","volume":"49","author":"Fishman L. 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