{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:35:45Z","timestamp":1788449745163,"version":"build-2803163510"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022RIS-005"],"award-info":[{"award-number":["2022RIS-005"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HI22C1498"],"award-info":[{"award-number":["HI22C1498"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-79610-w","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T01:58:55Z","timestamp":1731895135000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Comparative efficacy of anteroposterior and lateral X-ray based deep learning in the detection of osteoporotic vertebral compression fracture"],"prefix":"10.1038","volume":"14","author":[{"given":"Chulho","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minjae","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woon Tak","family":"Yuh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung-Lee","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Jun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong-Uk","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suk Hyung","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"key":"79610_CR1","doi-asserted-by":"publisher","first-page":"882241","DOI":"10.3389\/fendo.2022.882241","volume":"13","author":"Y Shen","year":"2022","unstructured":"Shen, Y. et al. The global burden of osteoporosis, low bone mass, and its related fracture in 204 countries and territories, 1990\u20132019. Front. Endocrinol. (Lausanne) 13, 882241. https:\/\/doi.org\/10.3389\/fendo.2022.882241 (2022).","journal-title":"Front. Endocrinol. (Lausanne)"},{"key":"79610_CR2","doi-asserted-by":"publisher","first-page":"20031","DOI":"10.1038\/s41598-020-76866-w","volume":"10","author":"K Murata","year":"2020","unstructured":"Murata, K. et al. Artificial intelligence for the detection of vertebral fractures on plain spinal radiography. Sci. Rep. 10, 20031. https:\/\/doi.org\/10.1038\/s41598-020-76866-w (2020).","journal-title":"Sci. Rep."},{"key":"79610_CR3","doi-asserted-by":"publisher","unstructured":"Soydan, Z. et al. An automatized deep segmentation and classification model for lumbar disk degeneration and clarification of its impact on clinical decisions. Glob. Spine J. 0, 21925682231200783, https:\/\/doi.org\/10.1177\/21925682231200783.","DOI":"10.1177\/21925682231200783"},{"key":"79610_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0245992","volume":"16","author":"HY Chen","year":"2021","unstructured":"Chen, H. Y. et al. Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs. PLoS One 16, e0245992. https:\/\/doi.org\/10.1371\/journal.pone.0245992 (2021).","journal-title":"PLoS One"},{"key":"79610_CR5","doi-asserted-by":"crossref","unstructured":"Nicolaes, J. et al. Detection of vertebral fractures in CT using 3D Convolutional Neural Networks. arXiv preprint arXiv:1911.01816 (2019).","DOI":"10.1016\/j.bonr.2020.100539"},{"key":"79610_CR6","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.future.2018.03.023","volume":"85","author":"U Raghavendra","year":"2018","unstructured":"Raghavendra, U., Bhat, N. S., Gudigar, A. & Acharya, U. R. Automated system for the detection of thoracolumbar fractures using a CNN architecture. Future Gener. Comput. Syst. 85, 184\u2013189. https:\/\/doi.org\/10.1016\/j.future.2018.03.023 (2018).","journal-title":"Future Gener. Comput. Syst."},{"key":"79610_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2021.116017","volume":"150","author":"BA Monchka","year":"2021","unstructured":"Monchka, B. A., Kimelman, D., Lix, L. M. & Leslie, W. D. Feasibility of a generalized convolutional neural network for automated identification of vertebral compression fractures: The Manitoba Bone Mineral Density Registry. Bone 150, 116017 (2021).","journal-title":"Bone"},{"key":"79610_CR8","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1007\/s00586-022-07121-1","volume":"31","author":"LR Yeh","year":"2022","unstructured":"Yeh, L. R. et al. A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: Retrospective training and validation of ResNet. Eur. Spine J. 31, 2022\u20132030. https:\/\/doi.org\/10.1007\/s00586-022-07121-1 (2022).","journal-title":"Eur. Spine J."},{"key":"79610_CR9","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.3208","volume":"10","author":"J Hatgis","year":"2018","unstructured":"Hatgis, J. et al. Radiologic evaluation of chronic vertebral compression fractures and role of vertebral augmentation. Cureus 10, e3208. https:\/\/doi.org\/10.7759\/cureus.3208 (2018).","journal-title":"Cureus"},{"key":"79610_CR10","unstructured":"Tan, M. & Le, Q. in International Conference on Machine Learning. 10096\u201310106 (PMLR)."},{"key":"79610_CR11","unstructured":"Cubuk, E. D., Zoph, B., Shlens, J. & Le, Q. V. in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 702\u2013703."},{"key":"79610_CR12","unstructured":"Kingma, D. P. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"79610_CR13","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84\u201390. https:\/\/doi.org\/10.1145\/3065386 (2017).","journal-title":"Commun. ACM"},{"key":"79610_CR14","doi-asserted-by":"publisher","first-page":"1496","DOI":"10.1007\/s00330-021-08247-4","volume":"32","author":"W Chen","year":"2022","unstructured":"Chen, W. et al. A deep-learning model for identifying fresh vertebral compression fractures on digital radiography. Eur. Radiol. 32, 1496\u20131505. https:\/\/doi.org\/10.1007\/s00330-021-08247-4 (2022).","journal-title":"Eur. Radiol."},{"key":"79610_CR15","unstructured":"Henkel, C. Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers. (2021)."},{"key":"79610_CR16","unstructured":"Fan, H. et al. in Proceedings of the IEEE\/CVF International Conference on Computer Vision. 6824\u20136835."},{"key":"79610_CR17","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.1002\/jbmr.4146","volume":"35","author":"N Kolanu","year":"2020","unstructured":"Kolanu, N. et al. Clinical utility of computer-aided diagnosis of vertebral fractures from computed tomography images. J. Bone Miner. Res. 35, 2307\u20132312. https:\/\/doi.org\/10.1002\/jbmr.4146 (2020).","journal-title":"J. Bone Miner. Res."},{"key":"79610_CR18","doi-asserted-by":"publisher","first-page":"1652","DOI":"10.1016\/j.spinee.2021.03.006","volume":"21","author":"A Yabu","year":"2021","unstructured":"Yabu, A. et al. Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images. Spine J. 21, 1652\u20131658. https:\/\/doi.org\/10.1016\/j.spinee.2021.03.006 (2021).","journal-title":"Spine J."},{"key":"79610_CR19","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1007\/s10278-021-00471-0","volume":"34","author":"DH Kim","year":"2021","unstructured":"Kim, D. H., Jeong, J. G., Kim, Y. J., Kim, K. G. & Jeon, J. Y. Automated vertebral segmentation and measurement of vertebral compression ratio based on deep learning in X-ray images. J. Digit Imaging 34, 853\u2013861. https:\/\/doi.org\/10.1007\/s10278-021-00471-0 (2021).","journal-title":"J. Digit Imaging"},{"key":"79610_CR20","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1016\/j.acra.2022.02.020","volume":"29","author":"Q Dong","year":"2022","unstructured":"Dong, Q. et al. Deep learning classification of spinal osteoporotic compression fractures on radiographs using an adaptation of the genant semiquantitative criteria. Acad. Radiol. 29, 1819\u20131832. https:\/\/doi.org\/10.1016\/j.acra.2022.02.020 (2022).","journal-title":"Acad. Radiol."},{"key":"79610_CR21","doi-asserted-by":"crossref","unstructured":"Lee, Y., Lee, E. & Jang, I. t. in 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 1\u20134.","DOI":"10.1109\/EMBC40787.2023.10340261"},{"key":"79610_CR22","doi-asserted-by":"publisher","first-page":"1142895","DOI":"10.3389\/frai.2023.1142895","volume":"6","author":"Z Li","year":"2023","unstructured":"Li, Z. et al. Bone age assessment based on deep neural networks with annotation-free cascaded critical bone region extraction. Front. Artif. Intell. 6, 1142895. https:\/\/doi.org\/10.3389\/frai.2023.1142895 (2023).","journal-title":"Front. Artif. Intell."},{"key":"79610_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2020.115561","volume":"140","author":"B Zhang","year":"2020","unstructured":"Zhang, B. et al. Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study. Bone 140, 115561. https:\/\/doi.org\/10.1016\/j.bone.2020.115561 (2020).","journal-title":"Bone"},{"key":"79610_CR24","doi-asserted-by":"crossref","unstructured":"Azizi, S. et al. in 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). 3458\u20133468.","DOI":"10.1109\/ICCV48922.2021.00346"},{"key":"79610_CR25","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar, M. et al. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321\u2013331 (2018).","journal-title":"Neurocomputing"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79610-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79610-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79610-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T02:06:45Z","timestamp":1732241205000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79610-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,18]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["79610"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-79610-w","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,18]]},"assertion":[{"value":"18 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2024","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"}}],"article-number":"28388"}}