{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T14:05:43Z","timestamp":1778940343642,"version":"3.51.4"},"reference-count":14,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Skeletal Radiol"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00256-023-04283-x","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T06:03:13Z","timestamp":1674021793000},"page":"1377-1384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1304-1576","authenticated-orcid":false,"given":"Connie Y.","family":"Chang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian A.","family":"Huber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaitlyn J.","family":"Yeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Colleen","family":"Buckless","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Torriani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"issue":"11","key":"4283_CR1","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1097\/01.CDR.0000558146.48072.2c","volume":"42","author":"Lucent Lesions of Vertebral Body","year":"2019","unstructured":"Lucent Lesions of Vertebral Body. Differential Diagnosis. Contemporary Diagnostic Radiology. 2019;42(11):8.","journal-title":"Contemporary Diagnostic Radiology."},{"key":"4283_CR2","doi-asserted-by":"crossref","unstructured":"Huber FA, Guggenberger R. AI MSK clinical applications: spine imaging. Skeletal Radiol. 2021;51.","DOI":"10.1007\/s00256-021-03862-0"},{"issue":"2","key":"4283_CR3","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s00256-021-03909-2","volume":"51","author":"GB Joseph","year":"2022","unstructured":"Joseph GB, McCulloch CE, Sohn JH, Pedoia V, Majumdar S, Link TM. AI MSK clinical applications: cartilage and osteoarthritis. Skeletal Radiol. 2022;51(2):331\u201343.","journal-title":"Skeletal Radiol."},{"issue":"2","key":"4283_CR4","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s00256-019-03284-z","volume":"49","author":"P Chea","year":"2020","unstructured":"Chea P, Mandell JC. Current applications and future directions of deep learning in musculoskeletal radiology. Skeletal Radiol. 2020;49(2):183\u201397.","journal-title":"Skeletal Radiol."},{"issue":"3","key":"4283_CR5","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1148\/radiol.2018180547","volume":"290","author":"S Soffer","year":"2019","unstructured":"Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional neural networks for radiologic images: a radiologist\u2019s guide. Radiology. 2019;290(3):590\u2013606.","journal-title":"Radiology."},{"key":"4283_CR6","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.ejrad.2019.02.038","volume":"114","author":"L Saba","year":"2019","unstructured":"Saba L, Biswas M, Kuppili V, Cuadrado Godia E, Suri HS, Edla DR, et al. The present and future of deep learning in radiology. Eur J Radiol. 2019;114:14\u201324.","journal-title":"Eur J Radiol."},{"issue":"2","key":"4283_CR7","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s00256-021-03873-x","volume":"51","author":"CY Chang","year":"2022","unstructured":"Chang CY, Buckless C, Yeh KJ, Torriani M. Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network. Skeletal Radiol. 2022;51(2):391\u20139.","journal-title":"Skeletal Radiol."},{"key":"4283_CR8","doi-asserted-by":"crossref","unstructured":"Faghani S, Baffour FI, Ringler MD, Hamilton-Cave M, Rouzrokh P, Moassefi M, et al. A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT. Skeletal Radiol. 2022;52.","DOI":"10.1007\/s00256-022-04160-z"},{"issue":"3","key":"4283_CR9","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s00256-019-03289-8","volume":"49","author":"R Hemke","year":"2020","unstructured":"Hemke R, Buckless CG, Tsao A, Wang B, Torriani M. Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment. Skeletal Radiol. 2020;49(3):387\u201395.","journal-title":"Skeletal Radiol."},{"issue":"1","key":"4283_CR10","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1002\/jbmr.3849","volume":"35","author":"JE Burns","year":"2020","unstructured":"Burns JE, Yao J, Summers RM. Artificial intelligence in musculoskeletal imaging: a paradigm shift. J Bone Miner Res. 2020;35(1):28\u201335.","journal-title":"J Bone Miner Res."},{"issue":"3","key":"4283_CR11","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1148\/radiol.2423060260","volume":"242","author":"SD O'Connor","year":"2007","unstructured":"O'Connor SD, Yao J, Summers RM. Lytic metastases in thoracolumbar spine: computer-aided detection at CT--preliminary study. Radiology. 2007;242(3):811\u20136.","journal-title":"Radiology."},{"key":"4283_CR12","doi-asserted-by":"crossref","unstructured":"Yao J, O'Connor S, Summers R. Computer aided lytic bone metastasis detection using regular CT images: SPIE, 2006.","DOI":"10.1117\/12.652288"},{"issue":"2","key":"4283_CR13","doi-asserted-by":"publisher","first-page":"362","DOI":"10.2214\/AJR.15.15559","volume":"207","author":"A Ulano","year":"2016","unstructured":"Ulano A, Bredella MA, Burke P, Chebib I, Simeone FJ, Huang AJ, et al. Distinguishing untreated osteoblastic metastases from enostoses using CT attenuation measurements. AJR Am J Roentgenol. 2016;207(2):362\u20138.","journal-title":"AJR Am J Roentgenol."},{"issue":"4","key":"4283_CR14","doi-asserted-by":"publisher","first-page":"782","DOI":"10.2214\/AJR.18.20526","volume":"212","author":"H Nakai","year":"2019","unstructured":"Nakai H, Arizono S, Isoda H, Togashi K. Imaging characteristics of liver metastases overlooked at contrast-enhanced CT. AJR Am J Roentgenol. 2019;212(4):782\u20137.","journal-title":"AJR Am J Roentgenol."}],"container-title":["Skeletal Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00256-023-04283-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00256-023-04283-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00256-023-04283-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T10:10:22Z","timestamp":1684231822000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00256-023-04283-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,18]]},"references-count":14,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["4283"],"URL":"https:\/\/doi.org\/10.1007\/s00256-023-04283-x","relation":{},"ISSN":["0364-2348","1432-2161"],"issn-type":[{"value":"0364-2348","type":"print"},{"value":"1432-2161","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,18]]},"assertion":[{"value":"3 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures performed in studies involving human participants were carried out in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was waived for individual participants included in the study. The study was approved by the local Institutional Review Board (IRB) and HIPAA compliant.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}