{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T12:36:35Z","timestamp":1793018195805,"version":"4.5.0"},"reference-count":29,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Beijing Jiaotong University\u2019s College Students\u2019 Innovative Entrepreneurial Training Plan Program"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Biomed Eng Comput Biol"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>The aim is to detect impacted teeth in panoramic radiology by refining the pretrained MedSAM model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study design:<\/jats:title>\n                    <jats:p>Impacted teeth are dental issues that can cause complications and are diagnosed via radiographs. We modified SAM model for individual tooth segmentation using 1016 X-ray images. The dataset was split into training, validation, and testing sets, with a ratio of 16:3:1. We enhanced the SAM model to automatically detect impacted teeth by focusing on the tooth\u2019s center for more accurate results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>With 200 epochs, batch size equals to 1, and a learning rate of 0.001, random images trained the model. Results on the test set showcased performance up to an accuracy of 86.73%, F1-score of 0.5350, and IoU of 0.3652 on SAM-related models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>This study fine-tunes MedSAM for impacted tooth segmentation in X-ray images, aiding dental diagnoses. Further improvements on model accuracy and selection are essential for enhancing dental practitioners\u2019 diagnostic capabilities.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/11795972241288319","type":"journal-article","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T05:02:21Z","timestamp":1728104541000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep Learning-Based Detection of Impacted Teeth on Panoramic Radiographs"],"prefix":"10.1177","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6233-440X","authenticated-orcid":false,"given":"He","family":"Zhicheng","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Yipeng","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,10,5]]},"reference":[{"key":"bibr1-11795972241288319","first-page":"131","volume":"2022","author":"Ness G","journal-title":"Impacted teeth. Peterson\u2019s principles of oral and maxillofacial surgery"},{"key":"bibr2-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1016\/j.jormas.2020.12.006"},{"key":"bibr3-11795972241288319","first-page":"525","volume-title":"Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event","volume":"2021","author":"Faure J"},{"key":"bibr4-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1016\/S1076-6332(03)80289-1"},{"key":"bibr5-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajoms.2022.02.004"},{"key":"bibr6-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2007.02.002"},{"key":"bibr7-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"bibr8-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01247-4"},{"key":"bibr9-11795972241288319","first-page":"1","volume":"35","author":"Zhu H","year":"2022","journal-title":"Neural Comput Appl"},{"key":"bibr10-11795972241288319","doi-asserted-by":"publisher","DOI":"10.3390\/app11199232"},{"key":"bibr11-11795972241288319","first-page":"341","volume-title":"Machine Intelligence and Data Science Applications: Proceedings of MIDAS 2021","volume":"2022","author":"Hossen R"},{"key":"bibr12-11795972241288319","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12051047"},{"key":"bibr13-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1134\/S1061830923600144"},{"key":"bibr14-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/7035367"},{"key":"bibr15-11795972241288319","doi-asserted-by":"publisher","DOI":"10.3390\/app12178500"},{"key":"bibr16-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-022-02119-z"},{"key":"bibr17-11795972241288319","doi-asserted-by":"publisher","DOI":"10.4012\/dmj.2022-098"},{"key":"bibr18-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1007\/s11282-021-00572-0"},{"key":"bibr19-11795972241288319","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2022.948283"},{"key":"bibr20-11795972241288319","doi-asserted-by":"publisher","DOI":"10.3390\/biom11060815"},{"key":"bibr21-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1007\/s11282-023-00677-8"},{"key":"bibr22-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1016\/j.oooo.2020.04.813"},{"key":"bibr23-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-29934-1"},{"key":"bibr24-11795972241288319","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12040942"},{"key":"bibr25-11795972241288319","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, et al Segment anything. arXiv preprint arXiv 2304.02643. 2023.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"bibr26-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-019-00227-x"},{"key":"bibr27-11795972241288319","unstructured":"Cheng J, Ye J, Deng Z, et al Sam-med2d. ArXiv preprint arXiv 2308.16184. 2023."},{"key":"bibr28-11795972241288319","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-44824-z"},{"key":"bibr29-11795972241288319","unstructured":"Hamamci IE, Er S, Simsar E, Yuksel, et al Dentex: An abnormal tooth detection with dental enumeration and diagnosis benchmark for panoramic x-rays. arXiv preprint arXiv 2305.19112. 2023."}],"container-title":["Biomedical Engineering and Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/11795972241288319","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/11795972241288319","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/11795972241288319","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T12:59:09Z","timestamp":1777381149000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/11795972241288319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":29,"alternative-id":["10.1177\/11795972241288319"],"URL":"https:\/\/doi.org\/10.1177\/11795972241288319","relation":{},"ISSN":["1179-5972","1179-5972"],"issn-type":[{"value":"1179-5972","type":"print"},{"value":"1179-5972","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]},"article-number":"11795972241288319"}}