{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T13:20:19Z","timestamp":1772371219419,"version":"3.50.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Natural Science Foundation of Shanghai","award":["18ZR1426900"],"award-info":[{"award-number":["18ZR1426900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the You Only Look Once (YOLO) network and verified the feasibility of this new method in aiding the detection of dental problems.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect 5 types of dental situations (impacted teeth, missing teeth, implants, crown restorations, and root canal-treated teeth) in panoramic radiography by improving the YOLO network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptively spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>About 1474 panoramic radiographs were divided into training, validation, and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP[0.5:0.95]) are 91.8% and 63.7%, respectively.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching, and clinical applications in the future.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/dmfr\/twae014","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T18:55:31Z","timestamp":1711133731000},"page":"296-307","source":"Crossref","is-referenced-by-count":4,"title":["DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs"],"prefix":"10.1093","volume":"53","author":[{"given":"Wei","family":"Li","sequence":"first","affiliation":[{"name":"School of Health Science and Engineering, University of Shanghai for Science and Technology , Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Health Science and Engineering, University of Shanghai for Science and Technology , Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shanghai Ninth People\u2019s Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai 200011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"issue":"4","key":"2024062808103789000_twae014-B1","doi-asserted-by":"crossref","first-page":"20180051","DOI":"10.1259\/dmfr.20180051","article-title":"Tooth detection and numbering in panoramic radiographs using convolutional neural networks","volume":"48","author":"Tuzoff","year":"2019","journal-title":"Dentomaxillofac Radiol"},{"issue":"4","key":"2024062808103789000_twae014-B2","doi-asserted-by":"crossref","first-page":"942","DOI":"10.3390\/diagnostics12040942","article-title":"Deep learning based detection tool for impacted mandibular third molar teeth","volume":"12","author":"Celik","year":"2022","journal-title":"Diagnostics (Basel)"},{"issue":"5","key":"2024062808103789000_twae014-B3","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1111\/ipd.12946","article-title":"Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study","volume":"32","author":"Mine","year":"2022","journal-title":"Int J Paediatr Dent"},{"issue":"4","key":"2024062808103789000_twae014-B4","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.oooo.2020.04.813","article-title":"Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs","volume":"130","author":"Kuwada","year":"2020","journal-title":"Oral Surg Oral Med Oral Pathol Oral Radiol"},{"issue":"1","key":"2024062808103789000_twae014-B5","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1186\/s12903-023-03532-8","article-title":"An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population","volume":"23","author":"Ba\u011f","year":"2023","journal-title":"BMC Oral Health"},{"issue":"3","key":"2024062808103789000_twae014-B6","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.3390\/app12031595","article-title":"Deep learning based detection of missing tooth regions for dental implant planning in panoramic radiographic images","volume":"12","author":"Park","year":"2022","journal-title":"Appl. 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