{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T23:40:23Z","timestamp":1782690023439,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T00:00:00Z","timestamp":1664323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research, Najran University, Kingdom of Saudi Arabia","award":["NU\/NRP\/MRC\/11\/21"],"award-info":[{"award-number":["NU\/NRP\/MRC\/11\/21"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.<\/jats:p>","DOI":"10.3390\/s22197370","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T01:23:16Z","timestamp":1664414596000},"page":"7370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7046-5311","authenticated-orcid":false,"given":"Yassir Edrees","family":"Almalki","sequence":"first","affiliation":[{"name":"Division of Radiology, Department of Internal Medicine, Medical College, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amsa Imam","family":"Din","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1770-8905","authenticated-orcid":false,"given":"Muhammad","family":"Ramzan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4161-6875","authenticated-orcid":false,"given":"Muhammad","family":"Irfan","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6852-7031","authenticated-orcid":false,"given":"Khalid Mahmood","family":"Aamir","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3064-3547","authenticated-orcid":false,"given":"Abdullah","family":"Almalki","sequence":"additional","affiliation":[{"name":"Department of Preventive Dental Sciences, College of Dentistry, Majmaah University, Al-Majmaah 11952, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saud","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Preventive Dental Sciences, College of Dentistry, Majmaah University, Al-Majmaah 11952, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghada","family":"Alaglan","sequence":"additional","affiliation":[{"name":"Department of Orthodontics and Pediatric Dentistry, College of Dentistry, Qassim University, Buraidah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0569-5887","authenticated-orcid":false,"given":"Hassan A","family":"Alshamrani","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7262-183X","authenticated-orcid":false,"given":"Saifur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Oprea, S., Marinescu, C., Lita, I., Jurianu, M., Visan, D.A., and Cioc, I.B. 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