{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:15:57Z","timestamp":1790100957974,"version":"4.0.1"},"reference-count":35,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T00:00:00Z","timestamp":1703635200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100005762","name":"National Taiwan University Hospital","doi-asserted-by":"publisher","award":["112\u2010UN0007"],"award-info":[{"award-number":["112\u2010UN0007"]}],"id":[{"id":"10.13039\/501100005762","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin Implant Dent Rel Res"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>This study aimed to use a deep learning (DL) approach for the automatic identification of the ridge deficiency around dental implants based on an image slice from cone\u2010beam computerized tomography (CBCT).<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>Single slices crossing the central long\u2010axis of 630 mandibular and 845 maxillary virtually placed implants (4\u20135\u2009mm diameter, 10\u2009mm length) in 412 patients were used. The ridges were classified based on the intraoral bone\u2010implant support and sinus floor location. The slices were either preprocessed by alveolar ridge homogenizing prior to DL (preprocessed) or left unpreprocessed. A convolutional neural network with ResNet\u201050 architecture was employed for DL.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The model achieved an accuracy of &gt;98.5% on the unpreprocessed image slices and was found to be superior to the accuracy observed on the preprocessed slices. On the mandible, model accuracy was 98.91\u2009\u00b1\u20091.45%, and F1 score, a measure of a model's accuracy in binary classification tasks, was lowest (97.30%) on the ridge with a combined horizontal\u2010vertical defect. On the maxilla, model accuracy was 98.82\u2009\u00b1\u20091.11%, and the ridge presenting an implant collar\u2010sinus floor distance of 5\u201310\u2009mm with a dehiscence defect had the lowest F1 score (95.86%). To achieve &gt;90% model accuracy, \u2265441 mandibular slices or \u2265592 maxillary slices were required.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The ridge deficiency around dental implants can be identified using DL from CBCT image slices without the need for preprocessed homogenization. The model will be further strengthened by implementing more clinical expertise in dental implant treatment planning and incorporating multiple slices to classify 3\u2010dimensional implant\u2010ridge relationships.<\/jats:p><\/jats:sec>","DOI":"10.1111\/cid.13301","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T06:52:20Z","timestamp":1703746340000},"page":"376-384","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep learning for the identification of ridge deficiency around dental implants"],"prefix":"10.1111","volume":"26","author":[{"given":"Cheng\u2010Hung","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering College of Technology and Engineering, National Taiwan Normal University  Taipei 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