{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T21:20:49Z","timestamp":1794345649989,"version":"4.9.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100002597","name":"Kyung Hee University","doi-asserted-by":"crossref","award":["KHU-20251299"],"award-info":[{"award-number":["KHU-20251299"]}],"id":[{"id":"10.13039\/501100002597","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["No. RS-2024-00421203"],"award-info":[{"award-number":["No. RS-2024-00421203"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["IITP-2021-0-02068, RS-2020-II201373, RS-2023-00220628"],"award-info":[{"award-number":["IITP-2021-0-02068, RS-2020-II201373, RS-2023-00220628"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-16760-5","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T21:04:20Z","timestamp":1756501460000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["DCNN models with post-hoc interpretability for the automated detection of glossitis and OSCC on the tongue"],"prefix":"10.1038","volume":"15","author":[{"given":"Yeon-Hee","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seonggwang","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junho","family":"Jung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Q. Schick","family":"Auh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Seo","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akhilanand","family":"Chaurasia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yung Kyun","family":"Noh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"16760_CR1","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/348272","volume":"2013","author":"J Kim","year":"2013","unstructured":"Kim, J. et al. Availability of tongue diagnosis system for assessing tongue coating thickness in patients with functional dyspepsia. Evid. Based Complement. Altern. Med. 2013, 348272. https:\/\/doi.org\/10.1155\/2013\/348272 (2013).","journal-title":"Evid. Based Complement. Altern. Med."},{"issue":"1","key":"16760_CR2","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1515\/mr-2021-0018","volume":"2021","author":"J Xie","year":"2021","unstructured":"Xie, J. et al. Digital tongue image analyses for health assessment. Med. Rev. 2021(1), 172\u2013198. https:\/\/doi.org\/10.1515\/mr-2021-0018 (2021).","journal-title":"Med. Rev."},{"key":"16760_CR3","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1054\/bjom.2000.0335","volume":"38","author":"N Mollaoglu","year":"2000","unstructured":"Mollaoglu, N. Oral lichen planus: a review. Br. J. Oral Maxillofac. Surg. 38, 370\u2013377. https:\/\/doi.org\/10.1054\/bjom.2000.0335 (2000).","journal-title":"Br. J. Oral Maxillofac. Surg."},{"key":"16760_CR4","unstructured":"R, A. N. & Rafiq, N. B. in StatPearls (StatPearls Publishing Copyright \u00a9 2025, StatPearls Publishing LLC., 2025)."},{"key":"16760_CR5","doi-asserted-by":"publisher","first-page":"e603","DOI":"10.4317\/medoral.23011","volume":"24","author":"BC Miguel\u00e1\u00f1ez-Medr\u00e1n","year":"2019","unstructured":"Miguel\u00e1\u00f1ez-Medr\u00e1n, B. C., Pozo-Kreilinger, J. J., Cebri\u00e1n-Carretero, J. L., Mart\u00ednez-Garc\u00eda, M. A. & L\u00f3pez-S\u00e1nchez, A. F. Oral squamous cell carcinoma of tongue: Histological risk assessment. A pilot study. Med. Oral Patol. Oral Cir. Bucal 24, e603\u2013e609. https:\/\/doi.org\/10.4317\/medoral.23011 (2019).","journal-title":"Med. Oral Patol. Oral Cir. Bucal"},{"key":"16760_CR6","doi-asserted-by":"publisher","first-page":"437","DOI":"10.2105\/ajph.2014.302495","volume":"105","author":"J Haber","year":"2015","unstructured":"Haber, J. et al. Putting the mouth back in the head: HEENT to HEENOT. Am. J. Public Health 105, 437\u2013441. https:\/\/doi.org\/10.2105\/ajph.2014.302495 (2015).","journal-title":"Am. J. Public Health"},{"key":"16760_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-33128-3_1","volume":"1213","author":"HP Chan","year":"2020","unstructured":"Chan, H. P., Samala, R. K., Hadjiiski, L. M. & Zhou, C. Deep learning in medical image analysis. Adv. Exp. Med. Biol. 1213, 3\u201321. https:\/\/doi.org\/10.1007\/978-3-030-33128-3_1 (2020).","journal-title":"Adv. Exp. Med. Biol."},{"key":"16760_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101411","volume":"65","author":"LA Dourado-Filho","year":"2021","unstructured":"Dourado-Filho, L. A. & Calumby, R. T. An experimental assessment of deep convolutional features for plant species recognition. Eco. Inform. 65, 101411. https:\/\/doi.org\/10.1016\/j.ecoinf.2021.101411 (2021).","journal-title":"Eco. Inform."},{"key":"16760_CR9","doi-asserted-by":"publisher","first-page":"1273253","DOI":"10.3389\/fpubh.2023.1273253","volume":"11","author":"M Li","year":"2023","unstructured":"Li, M., Jiang, Y., Zhang, Y. & Zhu, H. Medical image analysis using deep learning algorithms. Front. Public Health 11, 1273253. https:\/\/doi.org\/10.3389\/fpubh.2023.1273253 (2023).","journal-title":"Front. Public Health"},{"key":"16760_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph20053894","author":"RFT Gomes","year":"2023","unstructured":"Gomes, R. F. T. et al. Use of artificial intelligence in the classification of elementary oral lesions from clinical images. Int. J. Environ. Res. Public Health. https:\/\/doi.org\/10.3390\/ijerph20053894 (2023).","journal-title":"Int. J. Environ. Res. Public Health."},{"key":"16760_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13112766","author":"G Tanriver","year":"2021","unstructured":"Tanriver, G., Soluk Tekkesin, M. & Ergen, O. Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders. Cancers (Basel). https:\/\/doi.org\/10.3390\/cancers13112766 (2021).","journal-title":"Cancers (Basel)."},{"key":"16760_CR12","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/s0733-8635(02)00057-8","volume":"21","author":"JA Byrd","year":"2003","unstructured":"Byrd, J. A., Bruce, A. J. & Rogers, R. S. 3rd. Glossitis and other tongue disorders. Dermatol. Clin. 21, 123\u2013134. https:\/\/doi.org\/10.1016\/s0733-8635(02)00057-8 (2003).","journal-title":"Dermatol. Clin."},{"key":"16760_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/nu12113284","author":"D Risso","year":"2020","unstructured":"Risso, D., Drayna, D. & Morini, G. Alteration, reduction and taste loss: main causes and potential implications on dietary habits. Nutrients https:\/\/doi.org\/10.3390\/nu12113284 (2020).","journal-title":"Nutrients"},{"key":"16760_CR14","doi-asserted-by":"publisher","first-page":"E434","DOI":"10.1503\/cmaj.191331","volume":"192","author":"A Kobayashi","year":"2020","unstructured":"Kobayashi, A. & Iwasaki, H. Pernicious anemia presenting as glossitis. CMAJ 192, E434. https:\/\/doi.org\/10.1503\/cmaj.191331 (2020).","journal-title":"CMAJ"},{"key":"16760_CR15","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1186\/s12903-022-02464-z","volume":"22","author":"G-Y Chen","year":"2022","unstructured":"Chen, G.-Y., Tang, Z.-Q. & Bao, Z.-X. Vitamin B12 deficiency may play an etiological role in atrophic glossitis and its grading: A clinical case-control study. BMC Oral Health 22, 456. https:\/\/doi.org\/10.1186\/s12903-022-02464-z (2022).","journal-title":"BMC Oral Health"},{"key":"16760_CR16","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41368-023-00249-w","volume":"15","author":"Y Tan","year":"2023","unstructured":"Tan, Y. et al. Oral squamous cell carcinomas: state of the field and emerging directions. Int. J. Oral Sci. 15, 44. https:\/\/doi.org\/10.1038\/s41368-023-00249-w (2023).","journal-title":"Int. J. Oral Sci."},{"key":"16760_CR17","doi-asserted-by":"crossref","unstructured":"Nethan, S. T., Ravi, P. & Gupta, P. C. in Microbes and Oral Squamous Cell Carcinoma: A Network Spanning Infection and Inflammation (ed Samapika Routray) 1\u20137 (Springer Nature Singapore, 2022).","DOI":"10.1007\/978-981-19-0592-6_1"},{"key":"16760_CR18","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.oraloncology.2010.03.009","volume":"46","author":"J Bagan","year":"2010","unstructured":"Bagan, J., Sarrion, G. & Jimenez, Y. Oral cancer: Clinical features. Oral Oncol. 46, 414\u2013417. https:\/\/doi.org\/10.1016\/j.oraloncology.2010.03.009 (2010).","journal-title":"Oral Oncol."},{"key":"16760_CR19","doi-asserted-by":"publisher","first-page":"83","DOI":"10.5125\/jkaoms.2019.45.2.83","volume":"45","author":"YC Sim","year":"2019","unstructured":"Sim, Y. C., Hwang, J.-H. & Ahn, K.-M. Overall and disease-specific survival outcomes following primary surgery for oral squamous cell carcinoma: analysis of consecutive 67 patients. Jkaoms 45, 83\u201390. https:\/\/doi.org\/10.5125\/jkaoms.2019.45.2.83 (2019).","journal-title":"Jkaoms"},{"key":"16760_CR20","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s40902-024-00410-3","volume":"46","author":"MJ Kim","year":"2024","unstructured":"Kim, M. J. & Ahn, K. M. Prognostic factors of oral squamous cell carcinoma: the importance of recurrence and pTNM stage. Maxillofac. Plast. Reconstr. Surg. 46, 8. https:\/\/doi.org\/10.1186\/s40902-024-00410-3 (2024).","journal-title":"Maxillofac. Plast. Reconstr. Surg."},{"key":"16760_CR21","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.1200\/jco.23.00729","volume":"42","author":"JE Tota","year":"2024","unstructured":"Tota, J. E. et al. Inflammatory tongue conditions and risk of oral tongue cancer among the US elderly individuals. J. Clin. Oncol. 42, 1745\u20131753. https:\/\/doi.org\/10.1200\/jco.23.00729 (2024).","journal-title":"J. Clin. Oncol."},{"key":"16760_CR22","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118. https:\/\/doi.org\/10.1038\/nature21056 (2017).","journal-title":"Nature"},{"key":"16760_CR23","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122-1131.e1129. https:\/\/doi.org\/10.1016\/j.cell.2018.02.010 (2018).","journal-title":"Cell"},{"key":"16760_CR24","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2022","unstructured":"Li, Z., Liu, F., Yang, W., Peng, S. & Zhou, J. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33, 6999\u20137019. https:\/\/doi.org\/10.1109\/TNNLS.2021.3084827 (2022).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"16760_CR25","doi-asserted-by":"publisher","first-page":"18865","DOI":"10.1038\/s41598-024-69848-9","volume":"14","author":"Y-H Lee","year":"2024","unstructured":"Lee, Y.-H., Jeon, S., Won, J.-H., Auh, Q. S. & Noh, Y.-K. Automatic detection and visualization of temporomandibular joint effusion with deep neural network. Sci. Rep. 14, 18865. https:\/\/doi.org\/10.1038\/s41598-024-69848-9 (2024).","journal-title":"Sci. Rep."},{"key":"16760_CR26","doi-asserted-by":"publisher","first-page":"2999","DOI":"10.1007\/s12008-023-01327-3","volume":"17","author":"S Kumaresan","year":"2023","unstructured":"Kumaresan, S., Aultrin, K. S. J., Kumar, S. S. & Anand, M. D. Deep learning-based weld defect classification using VGG16 transfer learning adaptive fine-tuning. Int. J. Interact. Des. Manuf. (IJIDeM) 17, 2999\u20133010. https:\/\/doi.org\/10.1007\/s12008-023-01327-3 (2023).","journal-title":"Int. J. Interact. Des. Manuf. (IJIDeM)"},{"key":"16760_CR27","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s42979-020-0114-9","volume":"1","author":"D Theckedath","year":"2020","unstructured":"Theckedath, D. & Sedamkar, R. R. Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Comput. Sci. 1, 79. https:\/\/doi.org\/10.1007\/s42979-020-0114-9 (2020).","journal-title":"SN Comput. Sci."},{"key":"16760_CR28","doi-asserted-by":"crossref","unstructured":"Wen, L., Li, X., Li, X. & Gao, L. in 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD). 205\u2013209.","DOI":"10.1109\/CSCWD.2019.8791884"},{"key":"16760_CR29","doi-asserted-by":"publisher","first-page":"6111","DOI":"10.1007\/s00521-019-04097-w","volume":"32","author":"L Wen","year":"2020","unstructured":"Wen, L., Li, X. & Gao, L. A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput. Appl. 32, 6111\u20136124. https:\/\/doi.org\/10.1007\/s00521-019-04097-w (2020).","journal-title":"Neural Comput. Appl."},{"key":"16760_CR30","unstructured":"Roy, P., Chisty, M. M. O. & Fattah, H. M. A. in 2021 5th International Conference on Electrical Information and Communication Technology (EICT). 1\u20136."},{"key":"16760_CR31","unstructured":"Selvaraju, R. R. et al. in Proceedings of the IEEE international conference on computer vision. 618\u2013626."},{"key":"16760_CR32","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1002\/sim.2993","volume":"27","author":"EF Schisterman","year":"2008","unstructured":"Schisterman, E. F., Faraggi, D., Reiser, B. & Hu, J. Youden Index and the optimal threshold for markers with mass at zero. Stat. Med. 27, 297\u2013315. https:\/\/doi.org\/10.1002\/sim.2993 (2008).","journal-title":"Stat. Med."},{"key":"16760_CR33","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/s0001-2998(78)80014-2","volume":"8","author":"CE Metz","year":"1978","unstructured":"Metz, C. E. Basic principles of ROC analysis. Semin. Nucl. Med. 8, 283\u2013298. https:\/\/doi.org\/10.1016\/s0001-2998(78)80014-2 (1978).","journal-title":"Semin. Nucl. Med."},{"key":"16760_CR34","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/1471-2288-11-13","volume":"11","author":"AJ Vickers","year":"2011","unstructured":"Vickers, A. J., Cronin, A. M. & Begg, C. B. One statistical test is sufficient for assessing new predictive markers. BMC Med. Res. Methodol. 11, 13. https:\/\/doi.org\/10.1186\/1471-2288-11-13 (2011).","journal-title":"BMC Med. Res. Methodol."},{"key":"16760_CR35","doi-asserted-by":"crossref","unstructured":"Ku, W. L., Chou, H. C. & Peng, W. H. in 2015 Visual Communications and Image Processing (VCIP). 1\u20134.","DOI":"10.1109\/VCIP.2015.7457829"},{"key":"16760_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.oor.2024.100343","volume":"10","author":"AF Saber","year":"2024","unstructured":"Saber, A. F., Ahmed, S. K., Hussein, S. & Qurbani, K. Artificial intelligence-assisted nursing interventions in psychiatry for oral cancer patients: A concise narrative review. Oral Oncol. Rep. 10, 100343. https:\/\/doi.org\/10.1016\/j.oor.2024.100343 (2024).","journal-title":"Oral Oncol. Rep."},{"key":"16760_CR37","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.17329","volume":"12","author":"RTN Dos Santos","year":"2024","unstructured":"Dos Santos, R. T. N. et al. Use of digital strategies in the diagnosis of oral squamous cell carcinoma: A scoping review. PeerJ 12, e17329. https:\/\/doi.org\/10.7717\/peerj.17329 (2024).","journal-title":"PeerJ"},{"key":"16760_CR38","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1038\/s41598-024-51867-1","volume":"14","author":"R Haque","year":"2024","unstructured":"Haque, R., Hassan, M. M., Bairagi, A. K. & Shariful Islam, S. M. NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data. Sci. Rep. 14, 1524. https:\/\/doi.org\/10.1038\/s41598-024-51867-1 (2024).","journal-title":"Sci. Rep."},{"key":"16760_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.iswa.2022.200147","volume":"16","author":"IU Khan","year":"2022","unstructured":"Khan, I. U. et al. An effective approach to address processing time and computational complexity employing modified CCT for lung disease classification. Intell. Syst. Appl. 16, 200147. https:\/\/doi.org\/10.1016\/j.iswa.2022.200147 (2022).","journal-title":"Intell. Syst. Appl."},{"key":"16760_CR40","doi-asserted-by":"publisher","first-page":"100547","DOI":"10.1016\/j.prime.2024.100547","volume":"8","author":"M Pal","year":"2024","unstructured":"Pal, M., Parija, S. & Panda, G. An effective ensemble approach for classification of chest X-ray images having symptoms of COVID: A precautionary measure for the COVID-19 subvariants. e-Prime Adv. Electr. Eng. Electron. Energy 8, 100547. https:\/\/doi.org\/10.1016\/j.prime.2024.100547 (2024).","journal-title":"e-Prime Adv. Electr. Eng. Electron. Energy"},{"key":"16760_CR41","doi-asserted-by":"publisher","first-page":"22260","DOI":"10.1038\/s41598-024-73803-z","volume":"14","author":"F Celik","year":"2024","unstructured":"Celik, F., Celik, K. & Celik, A. Enhancing brain tumor classification through ensemble attention mechanism. Sci. Rep. 14, 22260. https:\/\/doi.org\/10.1038\/s41598-024-73803-z (2024).","journal-title":"Sci. Rep."},{"key":"16760_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2023.100335","volume":"14","author":"K Al-Thelaya","year":"2023","unstructured":"Al-Thelaya, K. et al. Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey. J. Pathol. Inf. 14, 100335. https:\/\/doi.org\/10.1016\/j.jpi.2023.100335 (2023).","journal-title":"J. Pathol. Inf."},{"key":"16760_CR43","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1002\/hed.20007","volume":"26","author":"CT Liao","year":"2004","unstructured":"Liao, C. T. et al. Telomerase as an independent prognostic factor in head and neck squamous cell carcinoma. Head Neck 26, 504\u2013512. https:\/\/doi.org\/10.1002\/hed.20007 (2004).","journal-title":"Head Neck"},{"key":"16760_CR44","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1016\/j.oraloncology.2006.10.003","volume":"43","author":"CT Liao","year":"2007","unstructured":"Liao, C. T. et al. Survival of second and multiple primary tumors in patients with oral cavity squamous cell carcinoma in the betel quid chewing area. Oral Oncol. 43, 811\u2013819. https:\/\/doi.org\/10.1016\/j.oraloncology.2006.10.003 (2007).","journal-title":"Oral Oncol."},{"key":"16760_CR45","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1007\/s12105-022-01441-w","volume":"16","author":"ECR Ferreira","year":"2022","unstructured":"Ferreira, E. C. R. et al. Oral squamous cell carcinoma frequency in young patients from referral centers around the world. Head Neck Pathol. 16, 755\u2013762. https:\/\/doi.org\/10.1007\/s12105-022-01441-w (2022).","journal-title":"Head Neck Pathol."},{"key":"16760_CR46","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s12105-008-0054-5","volume":"2","author":"S M\u00fcller","year":"2008","unstructured":"M\u00fcller, S., Pan, Y., Li, R. & Chi, A. C. Changing trends in oral squamous cell carcinoma with particular reference to young patients: 1971\u20132006. The Emory University experience. Head Neck Pathol. 2, 60\u201366. https:\/\/doi.org\/10.1007\/s12105-008-0054-5 (2008).","journal-title":"Head Neck Pathol."},{"key":"16760_CR47","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.job.2022.03.003","volume":"64","author":"M Tsuneki","year":"2022","unstructured":"Tsuneki, M. Deep learning models in medical image analysis. J. Oral Biosci. 64, 312\u2013320. https:\/\/doi.org\/10.1016\/j.job.2022.03.003 (2022).","journal-title":"J. Oral Biosci."},{"key":"16760_CR48","doi-asserted-by":"publisher","first-page":"796","DOI":"10.3390\/app11020796","volume":"11","author":"A Althnian","year":"2021","unstructured":"Althnian, A. et al. Impact of dataset size on classification performance: An empirical evaluation in the medical domain. Appl. Sci. 11, 796 (2021).","journal-title":"Appl. Sci."},{"key":"16760_CR49","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3390\/jimaging9040081","volume":"9","author":"A Kebaili","year":"2023","unstructured":"Kebaili, A., Lapuyade-Lahorgue, J. & Ruan, S. Deep learning approaches for data augmentation in medical imaging: A review. J. Imaging 9, 81 (2023).","journal-title":"J. Imaging"},{"key":"16760_CR50","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14161727","author":"M Davidian","year":"2024","unstructured":"Davidian, M. et al. Exploring the interplay of dataset size and imbalance on CNN performance in healthcare: Using X-rays to identify COVID-19 patients. Diagnostics (Basel). https:\/\/doi.org\/10.3390\/diagnostics14161727 (2024).","journal-title":"Diagnostics (Basel)."},{"key":"16760_CR51","unstructured":"Zaridis, D. G. et al. A new smart-cropping pipeline for prostate segmentation using deep learning networks. arxiv.abs\/2107.02476 (2021)."},{"key":"16760_CR52","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.bj.2020.10.008","volume":"44","author":"TM Abdelghany","year":"2021","unstructured":"Abdelghany, T. M. et al. SARS-CoV-2, the other face to SARS-CoV and MERS-CoV: Future predictions. Biomed. J. 44, 86\u201393. https:\/\/doi.org\/10.1016\/j.bj.2020.10.008 (2021).","journal-title":"Biomed. J."},{"key":"16760_CR53","doi-asserted-by":"publisher","first-page":"e159","DOI":"10.1097\/sla.0000000000004098","volume":"272","author":"CB Rosen","year":"2020","unstructured":"Rosen, C. B., Joffe, S. & Kelz, R. R. COVID-19 moves medicine into a virtual space: A paradigm shift from touch to talk to establish trust. Ann. Surg. 272, e159\u2013e160. https:\/\/doi.org\/10.1097\/sla.0000000000004098 (2020).","journal-title":"Ann. Surg."},{"key":"16760_CR54","doi-asserted-by":"publisher","first-page":"8108","DOI":"10.3390\/app14188108","volume":"14","author":"LS Wyatt","year":"2024","unstructured":"Wyatt, L. S., van Karnenbeek, L. M., Wijkhuizen, M., Geldof, F. & Dashtbozorg, B. Explainable artificial intelligence (XAI) for oncological ultrasound image analysis: A systematic review. Appl. Sci. 14, 8108 (2024).","journal-title":"Appl. Sci."},{"key":"16760_CR55","doi-asserted-by":"publisher","first-page":"11352","DOI":"10.1038\/s41598-022-15231-5","volume":"12","author":"Y-H Lee","year":"2022","unstructured":"Lee, Y.-H., Won, J. H., Kim, S., Auh, Q. S. & Noh, Y.-K. Advantages of deep learning with convolutional neural network in detecting disc displacement of the temporomandibular joint in magnetic resonance imaging. Sci. Rep. 12, 11352. https:\/\/doi.org\/10.1038\/s41598-022-15231-5 (2022).","journal-title":"Sci. Rep."},{"key":"16760_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102470","volume":"79","author":"BHM van der Velden","year":"2022","unstructured":"van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A. & Viergever, M. A. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med. Image Anal. 79, 102470. https:\/\/doi.org\/10.1016\/j.media.2022.102470 (2022).","journal-title":"Med. Image Anal."},{"key":"16760_CR57","first-page":"467","volume":"110","author":"L Straub","year":"2024","unstructured":"Straub, L., Schettini, P. & Myrex, P. Common tongue conditions in primary care. Am. Fam. Phys. 110, 467\u2013475 (2024).","journal-title":"Am. Fam. Phys."},{"key":"16760_CR58","doi-asserted-by":"publisher","first-page":"9","DOI":"10.51866\/rv.37","volume":"17","author":"E Zahid","year":"2022","unstructured":"Zahid, E., Bhatti, O., Zahid, M. A. & Stubbs, M. Overview of common oral lesions. Malays. Fam. Phys. 17, 9\u201321. https:\/\/doi.org\/10.51866\/rv.37 (2022).","journal-title":"Malays. Fam. Phys."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16760-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16760-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16760-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T21:41:29Z","timestamp":1757454089000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16760-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,29]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["16760"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-16760-5","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,29]]},"assertion":[{"value":"30 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The research protocol complied with the Declaration of Helsinki and was approved by the Institutional Review Board of Kyung Hee University Dental Hospital in Seoul, South Korea (IRB No-KH-23-015-001).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was obtained from all patients involved in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"31940"}}