{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T12:10:05Z","timestamp":1772453405581,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":21,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-37116-7","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T02:45:15Z","timestamp":1770605115000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning model for identification of metabolic bone disease of prematurity using wrist radiographs"],"prefix":"10.1038","volume":"16","author":[{"given":"Seul Gi","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seoi","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minwoo","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mi Jin","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang Won","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ee-Kyung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jung-Eun","family":"Cheon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyoun-Joong","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"issue":"3","key":"37116_CR1","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.jcte.2014.06.004","volume":"1","author":"SE Rustico","year":"2014","unstructured":"Rustico, S. E., Calabria, A. C. & Garber, S. J. Metabolic bone disease of prematurity. J. Clin. Transl Endocrinol. 1 (3), 85\u201391. https:\/\/doi.org\/10.1016\/j.jcte.2014.06.004 (2014).","journal-title":"J. Clin. Transl Endocrinol."},{"issue":"11","key":"37116_CR2","doi-asserted-by":"publisher","first-page":"2668","DOI":"10.1038\/s41372-021-01065-y","volume":"41","author":"S Kavurt","year":"2021","unstructured":"Kavurt, S. et al. Evaluation of radiologic evidence of metabolic bone disease in very low birth weight infants at fourth week of life. J. Perinatol. 41 (11), 2668\u20132673. https:\/\/doi.org\/10.1038\/s41372-021-01065-y (2021).","journal-title":"J. Perinatol."},{"key":"37116_CR3","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.clnesp.2017.09.008","volume":"23","author":"P Gaio","year":"2018","unstructured":"Gaio, P. et al. Incidence of metabolic bone disease in preterm infants of birth weight\u2009<\u20091250\u00a0g and in those suffering from bronchopulmonary dysplasia. Clin. Nutr. ESPEN. 23, 234\u2013239. https:\/\/doi.org\/10.1016\/j.clnesp.2017.09.008 (2018).","journal-title":"Clin. Nutr. ESPEN"},{"issue":"12","key":"37116_CR4","doi-asserted-by":"publisher","first-page":"3786","DOI":"10.3390\/nu12123786","volume":"12","author":"A Avila-Alvarez","year":"2020","unstructured":"Avila-Alvarez, A. et al. Metabolic bone disease of prematurity: risk factors and associated short-term outcomes. Nutrients 12 (12), 3786. https:\/\/doi.org\/10.3390\/nu12123786 (2020).","journal-title":"Nutrients"},{"issue":"4","key":"37116_CR5","doi-asserted-by":"publisher","first-page":"332","DOI":"10.4274\/jcrpe.galenos.2019.2019.0091","volume":"12","author":"S Chacham","year":"2020","unstructured":"Chacham, S., Pasi, R., Chegondi, M., Ahmad, N. & Mohanty, S. B. Metabolic bone disease in premature neonates: an unmet challenge. J. Clin. Res. Pediatr. Endocrinol. 12 (4), 332\u2013339. https:\/\/doi.org\/10.4274\/jcrpe.galenos.2019.2019.0091 (2020).","journal-title":"J. Clin. Res. Pediatr. Endocrinol."},{"key":"37116_CR6","doi-asserted-by":"publisher","first-page":"143","DOI":"10.3389\/fped.2019.00143","volume":"7","author":"MF Faienza","year":"2019","unstructured":"Faienza, M. F. et al. Metabolic bone disease of prematurity: diagnosis and management. Front. Pediatr. 7, 143. https:\/\/doi.org\/10.3389\/fped.2019.00143 (2019).","journal-title":"Front. Pediatr."},{"issue":"11","key":"37116_CR7","doi-asserted-by":"publisher","first-page":"e631","DOI":"10.1542\/neo.16-11-e631","volume":"16","author":"A Moreira","year":"2015","unstructured":"Moreira, A., Jacob, R., Lavender, L. & Escaname, E. Metabolic bone disease of prematurity. NeoReviews 16 (11), e631\u2013641. https:\/\/doi.org\/10.1542\/neo.16-11-e631 (2015).","journal-title":"NeoReviews"},{"issue":"5","key":"37116_CR8","doi-asserted-by":"publisher","first-page":"F560","DOI":"10.1136\/archdischild-2018-316330","volume":"104","author":"A Chinoy","year":"2019","unstructured":"Chinoy, A., Mughal, M. Z. & Padidela, R. Metabolic bone disease of prematurity: causes, recognition, prevention, treatment and long-term consequences. Arch. Dis. Child. Fetal Neonatal Ed. 104 (5), F560\u2013F566. https:\/\/doi.org\/10.1136\/archdischild-2018-316330 (2019).","journal-title":"Arch. Dis. Child. Fetal Neonatal Ed."},{"issue":"5","key":"37116_CR9","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1177\/1941406415601482","volume":"7","author":"K Stewart","year":"2015","unstructured":"Stewart, K., Rittenhouse, M., Gloeckner, J. & Torisky, D. Screening for metabolic bone disease in preterm infants. Infant Child. Adolesc. Nutr. 7 (5), 229\u2013232. https:\/\/doi.org\/10.1177\/1941406415601482 (2015).","journal-title":"Infant Child. Adolesc. Nutr."},{"issue":"1","key":"37116_CR10","doi-asserted-by":"publisher","first-page":"101086","DOI":"10.1016\/j.siny.2020.101086","volume":"25","author":"A Rayannavar","year":"2020","unstructured":"Rayannavar, A. & Calabria, A. C. Screening for metabolic bone disease of prematurity. Semin Fetal Neonatal Med. 25 (1), 101086. https:\/\/doi.org\/10.1016\/j.siny.2020.101086 (2020).","journal-title":"Semin Fetal Neonatal Med."},{"issue":"6","key":"37116_CR11","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1148\/rg.2016160004","volume":"36","author":"CY Chang","year":"2016","unstructured":"Chang, C. Y. et al. Imaging findings of metabolic bone disease. Radiographics 36 (6), 1871\u20131887. https:\/\/doi.org\/10.1148\/rg.2016160004 (2016).","journal-title":"Radiographics"},{"issue":"6","key":"37116_CR12","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1136\/adc.57.6.447","volume":"57","author":"WW Koo","year":"1982","unstructured":"Koo, W. W., Gupta, J. M., Nayanar, V. V., Wilkinson, M. & Posen, S. Skeletal changes in preterm infants. Arch. Dis. Child. 57 (6), 447\u2013452. https:\/\/doi.org\/10.1136\/adc.57.6.447 (1982).","journal-title":"Arch. Dis. Child."},{"issue":"2","key":"37116_CR13","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1148\/rg.2017160130","volume":"37","author":"BJ Erickson","year":"2017","unstructured":"Erickson, B. J., Korfiatis, P., Akkus, Z. & Kline, T. L. Machine learning for medical imaging. Radiographics 37 (2), 505\u2013515. https:\/\/doi.org\/10.1148\/rg.2017160130 (2017).","journal-title":"Radiographics"},{"issue":"4","key":"37116_CR14","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s00247-018-4277-7","volume":"49","author":"MM Moore","year":"2019","unstructured":"Moore, M. M., Slonimsky, E., Long, A. D., Sze, R. W. & Iyer, R. S. Machine learning concepts, concerns and opportunities for a pediatric radiologist. Pediatr. Radiol. 49 (4), 509\u2013516. https:\/\/doi.org\/10.1007\/s00247-018-4277-7 (2019).","journal-title":"Pediatr. Radiol."},{"issue":"3","key":"37116_CR15","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1148\/radiol.2017171734","volume":"285","author":"CE Kahn","year":"2017","unstructured":"Kahn, C. E. Jr. From images to actions: opportunities for artificial intelligence in radiology. Radiology 285 (3), 719\u2013720. https:\/\/doi.org\/10.1148\/radiol.2017171734 (2017).","journal-title":"Radiology"},{"issue":"7","key":"37116_CR16","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1148\/rg.2017170077","volume":"37","author":"G Chartrand","year":"2017","unstructured":"Chartrand, G. et al. Deep learning: A primer for radiologists. Radiographics 37 (7), 2113\u20132131. https:\/\/doi.org\/10.1148\/rg.2017170077 (2017).","journal-title":"Radiographics"},{"key":"37116_CR17","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O. et al. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y (2015).","journal-title":"Int. J. Comput. Vis."},{"issue":"2","key":"37116_CR18","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/info11020125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev, A. et al. Albumentations: fast and flexible image augmentations. Information 11 (2), 125. https:\/\/doi.org\/10.3390\/info11020125 (2020).","journal-title":"Information"},{"issue":"2","key":"37116_CR19","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1145\/3065386","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2), 1097\u20131105. https:\/\/doi.org\/10.1145\/3065386 (2012).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"37116_CR20","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1404.1869","author":"F Iandola","year":"2014","unstructured":"Iandola, F. et al. DenseNet: implementing efficient ConvNet descriptor pyramids. Preprint at. https:\/\/doi.org\/10.48550\/arXiv.1404.1869 (2014).","journal-title":"Preprint at."},{"key":"37116_CR21","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. 2016 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR). 770-778 https:\/\/doi.org\/10.1109\/CVPR.2016.90 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"37116_CR22","doi-asserted-by":"publisher","unstructured":"Xie, S. et al. Aggregated residual transformations for deep neural networks. IEEE Conference on Computer Vision and (CVPR), 5987\u20135995; (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.634 (2017).","DOI":"10.1109\/CVPR.2017.634"},{"key":"37116_CR23","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1711.05225","author":"P Rajpurkar","year":"2017","unstructured":"Rajpurkar, P. et al. Radiologist-level pneumonia detection on chest X-rays with deep learning. Preprint at. https:\/\/doi.org\/10.48550\/arXiv.1711.05225 (2017).","journal-title":"Preprint at."},{"key":"37116_CR24","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.1556","author":"K Simonyan","year":"2015","unstructured":"Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at. https:\/\/doi.org\/10.48550\/arXiv.1409.1556 (2015).","journal-title":"Preprint at."},{"key":"37116_CR25","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1905.11946","author":"M Tan","year":"2019","unstructured":"Tan, M., Le, Q. V. & EfficientNet Rethinking model scaling for convolutional neural networks. Preprint at. https:\/\/doi.org\/10.48550\/arXiv.1905.11946 (2019).","journal-title":"Preprint at."},{"issue":"1","key":"37116_CR26","doi-asserted-by":"publisher","first-page":"57","DOI":"10.4258\/hir.2025.31.1.57","volume":"31","author":"N Sehar","year":"2025","unstructured":"Sehar, N., Krishnamoorthi, N. & Vinoth Kumar, C. Deep learning Model-Based detection of anemia from conjunctiva images. Healthc. Inf. Res. 31 (1), 57\u201365. https:\/\/doi.org\/10.4258\/hir.2025.31.1.57 (2025).","journal-title":"Healthc. Inf. Res."},{"key":"37116_CR27","doi-asserted-by":"publisher","unstructured":"Shermin, T. et al. Enhanced transfer learning with ImageNet trained classification layer in Image and Video Technology PSIVT Lecture notes in Computer Science, vol. 11854 (Springer, Cham., 2019); (2019). (ed. Lee, C., Su, Z. & Sugimoto, A.) https:\/\/doi.org\/10.1007\/978-3-030-34879-3_12","DOI":"10.1007\/978-3-030-34879-3_12"},{"key":"37116_CR28","doi-asserted-by":"publisher","first-page":"8024","DOI":"10.48550\/arXiv.1912.01703","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8024\u20138035. https:\/\/doi.org\/10.48550\/arXiv.1912.01703 (2019).","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"2","key":"37116_CR29","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1038\/pr.2017.46","volume":"82","author":"MC Walsh","year":"2017","unstructured":"Walsh, M. C. et al. Neonatal outcomes of moderately preterm infants compared to extremely preterm infants. Pediatr. Res. 82 (2), 297\u2013304. https:\/\/doi.org\/10.1038\/pr.2017.46 (2017).","journal-title":"Pediatr. Res."},{"issue":"1","key":"37116_CR30","doi-asserted-by":"publisher","first-page":"38","DOI":"10.2214\/AJR.18.20224","volume":"212","author":"GS Handelman","year":"2019","unstructured":"Handelman, G. S. et al. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. AJR Am. J. Roentgenol. 212 (1), 38\u201343. https:\/\/doi.org\/10.2214\/AJR.18.20224 (2019).","journal-title":"AJR Am. J. Roentgenol."},{"issue":"2","key":"37116_CR31","doi-asserted-by":"publisher","first-page":"140","DOI":"10.4258\/hir.2024.30.2.140","volume":"30","author":"BK Jang","year":"2024","unstructured":"Jang, B. K. & Park, Y. R. Development and validation of adaptable skin cancer classification system using dynamically expandable representation. Healthc. Inf. Res. 30 (2), 140\u2013146. https:\/\/doi.org\/10.4258\/hir.2024.30.2.140 (2024).","journal-title":"Healthc. Inf. Res."},{"issue":"3","key":"37116_CR32","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44 (3), 837\u2013845 (1988).","journal-title":"Biometrics"},{"key":"37116_CR33","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1186\/1471-2105-12-77","volume":"12","author":"X Robin","year":"2011","unstructured":"Robin, X. et al. pROC: an open-source package for R and S\u2009+\u2009to analyze and compare ROC curves. BMC Bioinform. 12, 77. https:\/\/doi.org\/10.1186\/1471-2105-12-77 (2011).","journal-title":"BMC Bioinform."},{"issue":"7","key":"37116_CR34","doi-asserted-by":"publisher","first-page":"4518","DOI":"10.1002\/mp.15551","volume":"49","author":"BT Dao","year":"2022","unstructured":"Dao, B. T., Nguyen, T. V., Pham, H. H. & Nguyen, H. Q. Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling. Med. Phys. 49 (7), 4518\u20134528. https:\/\/doi.org\/10.1002\/mp.15551 (2022).","journal-title":"Med. Phys."},{"issue":"7","key":"37116_CR35","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1007\/s00247-014-3268-6","volume":"45","author":"AE Oestreich","year":"2015","unstructured":"Oestreich, A. E. Concave distal end of ulna metaphysis alone is not a sign of rickets. Pediatr. Radiol. 45 (7), 998\u20131000. https:\/\/doi.org\/10.1007\/s00247-014-3268-6 (2015).","journal-title":"Pediatr. Radiol."},{"issue":"5","key":"37116_CR36","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1007\/s00247-020-04895-8","volume":"51","author":"KC Meda","year":"2021","unstructured":"Meda, K. C., Milla, S. S. & Rostad, B. S. Artificial intelligence research within reach: an object detection model to identify rickets on pediatric wrist radiographs. Pediatr. Radiol. 51 (5), 782\u2013791. https:\/\/doi.org\/10.1007\/s00247-020-04895-8 (2021).","journal-title":"Pediatr. Radiol."},{"key":"37116_CR37","doi-asserted-by":"publisher","unstructured":"Khanam, R. & Hussain YOLOv11: An overview of the Key Architectural Enhancements. Preprint at (2024). https:\/\/doi.org\/10.48550\/arXiv.2410.17725","DOI":"10.48550\/arXiv.2410.17725"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37116-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37116-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37116-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:38:21Z","timestamp":1772451501000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-37116-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["37116"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-37116-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]},"assertion":[{"value":"31 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2026","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":"7885"}}