{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:39:53Z","timestamp":1791913193385,"version":"4.3.4"},"reference-count":33,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"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":["Clinical &amp;amp; Exp Dental Res"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Benign fibro\u2010osseous lesions (BFOL) constitute a group of pathologic entities with marked overlapping histopathologic features but are diverse in nature and clinical behaviors. Accurate diagnoses of BFOLs necessitate clinical\u2010pathological correlations, which are paramount for their appropriate management. Recent research indicates the potential utility of artificial intelligence in diagnostic pathology. Here, we aimed to assess the performance of the deep convolutional neural network (DCNN) models for BFOL classification and investigate its impact on the diagnostic performance of oral pathologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>Microscopic slides from 68 patients diagnosed with cemento\u2010ossifying fibroma (COF), fibrous dysplasia (FD), and cemento\u2010osseous dysplasia (COD) were collected. The image patches from each slide were processed, augmented, and used to train and validate the five pre\u2010trained DCNN models for BFOL classification. The best\u2010performing model was selected to evaluate its diagnostic performance on the testing data set, compared with experienced oral pathologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The InceptionV3 model showed the highest and most balanced overall performance in BFOL classification. It demonstrated the highest accuracy (96.7%) in classifying COF, followed by COD (83.3%), and FD (80.0%), respectively. The model accuracy in identifying COF was greater than the average performance of pathologists (90.0%). However, pathologists performed better in classifying COD (87.2%) and FD (95.0%). With DCNN assistance, pathologists significantly improved the accuracy, sensitivity, and specificity in distinguishing BFOLs while reducing the average diagnosis time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The DCNN model has the potential to be developed as an auxiliary tool, assisting pathologists in diagnosing BFOLs. Through ongoing refinements, artificial intelligence assistance can aid pathologists in enhancing the accuracy and efficiency of BFOL diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cre2.70244","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T06:27:17Z","timestamp":1760682437000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Assessment of Deep Convolutional Neural Network Models for the Classification of Benign Fibro\u2010Osseous Lesions of the Jaws"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2753-7580","authenticated-orcid":false,"given":"Paniti","family":"Achararit","sequence":"first","affiliation":[{"name":"Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy Bangkok Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8961-7901","authenticated-orcid":false,"given":"Chawan","family":"Manaspon","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Institute Chiang Mai University Chiang Mai Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chavin","family":"Jongwannasiri","sequence":"additional","affiliation":[{"name":"Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy Bangkok Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kraisorn","family":"Sappayatosok","sequence":"additional","affiliation":[{"name":"College of Dental Medicine Rangsit University Pathum Thani Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1649-6357","authenticated-orcid":false,"given":"Thanaphum","family":"Osathanon","sequence":"additional","affiliation":[{"name":"Center of Excellence for Dental Stem Cell Biology, Department of Anatomy, Faculty of Dentistry Chulalongkorn University Bangkok Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ekarat","family":"Phattarataratip","sequence":"additional","affiliation":[{"name":"Department of Oral Pathology, Faculty of Dentistry Chulalongkorn University Bangkok Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12105-024-01643-4"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/jop.13477"},{"key":"e_1_2_11_4_1","unstructured":"Bello I. W.Fedus andX.Du et al.2021. \u201cRevisiting ResNets: Improved Training and Scaling Strategies.\u201d InProceedings of the 35th International Conference on Neural Information Processing Systems 22614\u201322627."},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/odi.12563"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41368-024-00287-y"},{"key":"e_1_2_11_7_1","doi-asserted-by":"crossref","unstructured":"Chollet F.2017. Xception: Deep Learning With Depthwise Separable Convolutions. 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