{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:39:11Z","timestamp":1791819551313,"version":"4.3.4"},"reference-count":34,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Dental Education"],"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 investigate the caries diagnosis performances of dental students after training with an artificial intelligence (AI) application utilizing deep learning techniques, a type of artificial neural network.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 1200 bitewing radiographs were obtained from the institution's database and two specialist dentists labeled the caries lesions in the images. Randomly selected 1000 images were used for training purposes and the remaining 200 radiographs were used to evaluate the caries diagnostic performance of the AI. Then, a convolutional neural network, a deep learning algorithm commonly employed to analyze visual imagery problems, called \u201cYou Only Look Once,\u201d was modified and trained to detect enamel and dentin caries lesions in the radiographs. Forty dental students were selected voluntarily and randomly divided into two groups. The pre\u2010test results of dental caries diagnosis performances of both groups were recorded. After 1 week, group 2 students were trained using an AI application. Then, the post\u2010test results of both groups were recorded. The labeling duration of the students was also measured and analyzed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>When both groups\u2019 pre\u2010test and post\u2010test results were evaluated, a statistically significant improvement was found for all parameters examined except precision score (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.05). However, the trained group's accuracy, sensitivity, specificity, and F1 scores were significantly higher than the non\u2010trained group in terms of post\u2010test scores (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.05). In group 2 (trained group), the post\u2010test labeling time was considerably increased (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The students trained by AI showed promising results in detecting caries lesions. The use of AI can also contribute to the clinical education of dental students.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jdd.13437","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T04:15:23Z","timestamp":1704946523000},"page":"490-500","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Dental student application of artificial intelligence technology in detecting proximal caries lesions"],"prefix":"10.1002","volume":"88","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5463-8064","authenticated-orcid":false,"given":"Enes","family":"Ayan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering Faculty of Engineering and Architecture K\u0131r\u0131kkale University  K\u0131r\u0131kkale Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6250-5651","authenticated-orcid":false,"given":"Yusuf","family":"Bayraktar","sequence":"additional","affiliation":[{"name":"Department of Restorative Dentistry Faculty of Dentistry K\u0131r\u0131kkale University  K\u0131r\u0131kkale Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5936-0196","authenticated-orcid":false,"given":"\u00c7i\u011fdem","family":"\u00c7elik","sequence":"additional","affiliation":[{"name":"Department of Restorative Dentistry Faculty of Dentistry K\u0131r\u0131kkale University  K\u0131r\u0131kkale Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7488-895X","authenticated-orcid":false,"given":"Baturalp","family":"Ayhan","sequence":"additional","affiliation":[{"name":"Department of Restorative Dentistry Faculty of Dentistry K\u0131r\u0131kkale University  K\u0131r\u0131kkale Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/154405910408301s27"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(07)60031-2"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsurg.2019.05.015"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-021-06127-z"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-90428-8"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104418"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00815-1"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00784-021-04040-1"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2020.103425"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2018.07.015"},{"issue":"6","key":"e_1_2_10_13_1","first-page":"719","article-title":"The effect of experience and training on the diagnosis of approximal coronal caries from bitewing radiographs. A Swiss\u2010American comparison","volume":"104","author":"Firestone AR","year":"1994","journal-title":"Schweiz Monatsschr Zahnmed"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41415-021-3526-6"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aau3877"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-016-0487-4"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/mbe.12109"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.01.021"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2015.02.014"},{"key":"e_1_2_10_21_1","doi-asserted-by":"crossref","unstructured":"RedmonJ DivvalaS GirshickR FarhadiA.You only look once: unified real\u2010time object detection.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:779\u2010788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_2_10_22_1","doi-asserted-by":"crossref","unstructured":"RedmonJ FarhadiA.YOLO9000: better faster stronger.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:7263\u20107271.","DOI":"10.1109\/CVPR.2017.690"},{"key":"e_1_2_10_23_1","unstructured":"RedmonJ FarhadiA.Yolov3: an incremental improvement.arXiv preprint arXiv:1804.02767.2018."},{"key":"e_1_2_10_24_1","unstructured":"BochkovskiyA WangC\u2010Y LiaoH\u2010YM.Yolov4: optimal speed and accuracy of object detection.arXiv preprint arXiv:2004.10934.2020."},{"key":"e_1_2_10_25_1","unstructured":"YOLO\u2010V5. Accessed April 14 2022.https:\/\/github.com\/ultralytics\/yolov5"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr\/30526171"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0192-5"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17093269"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.3352\/jeehp.2019.16.7"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.1177\/1553350618777045"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00592-y"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.2196\/19285"},{"key":"e_1_2_10_33_1","first-page":"41","volume-title":"Multimodal semantic segmentation: fusion of RGB and depth data in convolutional neural networks","author":"Koppanyi Z","year":"2019"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1177\/00220345211032524"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2018.04.022"}],"container-title":["Journal of Dental Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jdd.13437","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T12:00:57Z","timestamp":1713355257000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jdd.13437"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,10]]},"references-count":34,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["10.1002\/jdd.13437"],"URL":"https:\/\/doi.org\/10.1002\/jdd.13437","archive":["Portico"],"relation":{},"ISSN":["0022-0337","1930-7837"],"issn-type":[{"value":"0022-0337","type":"print"},{"value":"1930-7837","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,10]]},"assertion":[{"value":"2023-04-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-15","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}