{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T02:12:04Z","timestamp":1793153524776,"version":"4.5.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100020639","name":"Bayerische Staatsministerium f\u00fcr Wirtschaft, Landesentwicklung und Energie","doi-asserted-by":"publisher","award":["LSM-2403-0014"],"award-info":[{"award-number":["LSM-2403-0014"]}],"id":[{"id":"10.13039\/501100020639","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Vertebral fractures are severe complications of osteoporosis but are frequently missed on computed tomography (CT). Differentiating true fractures from non-osteoporotic vertebral height loss remains challenging; deep learning (DL) models may improve detection and grading.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>In this retrospective study, we evaluated eight human raters with different expertise (three students, three residents, and two attendings), four DL models, and one DL-based commercial software using the public Vertebral Segmentation (VerSe) 19 &amp; 20 datasets. Vertebral fractures were graded using the semiquantitative Genant scale (0\u20133). Diagnostic performance was evaluated using interrater agreement and classification metrics, with significance tested via generalized linear mixed models across patient and vertebral levels for the thoracolumbar spine and its regional subsets. Consensus readings by a senior neuroradiologist and an experienced resident, informed by clinical data, served as the reference standard.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      3548 thoracic and lumbar vertebrae from 331 patients were analyzed. 190 (5.4%) of vertebrae were fractured, and 139 (3.9%) had \u201cclinically most relevant\u201d moderate or severe fractures (Genant 2 or 3). DL models showed comparable accuracy as residents in detecting moderate\/severe fractures (0.988 vs. 0.991,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&gt;\u20090.05)\u00a0on vetebral level. SpineQ v1.1 consistently showed comparable Area Under the Curve (AUROC) and higher diagnostic accuracy compared to experts in detecting any fracture and moderate\/severe fractures across vertebral, regional, and patient-level analyses. Students consistently exhibited the lowest AUROC and diagnostic accuracy across all levels and comparisons.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>When specifically trained to detect a distinct condition like vertebral fractures, advanced algorithms can show comparable performance as experts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Can dedicated deep learning-based algorithms improve diagnostic performance for accurate detection and grading of osteoporotic vertebral fractures on CT scans?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Deep learning models specifically trained for vertebral fracture detection and grading can reach comparable performance as experts for the identification and grading of osteoporotic fractures.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Specifically trained deep learning models represent a valuable advancement for improving the identification and grading of osteoporotic fractures in clinical practice, bringing these tools a significant step closer to routine clinical implementation.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12393-y","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T04:58:15Z","timestamp":1771909095000},"page":"7556-7571","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Diagnostic accuracy of deep learning vs. human raters for detecting osteoporotic vertebral compression fractures in routine CT scans"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2259-9221","authenticated-orcid":false,"given":"Evamaria O.","family":"Riedel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Schinz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Keicher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"R\u00fchling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malek","family":"El Husseini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chantal","family":"Pellegrini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Baum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Dieckmeyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Malagutti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabel","family":"Seeger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna S.","family":"Walburga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benedikt","family":"Wiestler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nico","family":"Sollmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian T.","family":"L\u00f6ffler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arthur","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan S.","family":"Kirschke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"12393_CR1","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1093\/oxfordjournals.aje.a116756","volume":"137","author":"C Cooper","year":"1993","unstructured":"Cooper C, Atkinson EJ, Jacobsen SJ, O\u2019Fallon WM, Melton LJ 3rd (1993) Population-based study of survival after osteoporotic fractures. Am J Epidemiol 137:1001\u20131005","journal-title":"Am J Epidemiol"},{"key":"12393_CR2","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s00198-012-2074-y","volume":"24","author":"JA Kanis","year":"2013","unstructured":"Kanis JA, McCloskey EV, Johansson H, Cooper C, Rizzoli R, Reginster JY (2013) European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int 24:23\u201357","journal-title":"Osteoporos Int"},{"key":"12393_CR3","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1016\/S0140-6736(02)08657-9","volume":"359","author":"SR Cummings","year":"2002","unstructured":"Cummings SR, Melton LJ (2002) Epidemiology and outcomes of osteoporotic fractures. Lancet 359:1761\u20131767","journal-title":"Lancet"},{"key":"12393_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.bone.2025.117735","volume":"203","author":"M Behanova","year":"2026","unstructured":"Behanova M, Sokhan A, Haschka J et al (2026) AI-supported opportunistic detection of vertebral fractures on routine CT scans: diagnostic performance and clinical relevance. Bone 203:117735","journal-title":"Bone"},{"key":"12393_CR5","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s00198-011-1664-4","volume":"23","author":"R Wustrack","year":"2012","unstructured":"Wustrack R, Seeman E, Bucci-Rechtweg C, Burch S, Palermo L, Black DM (2012) Predictors of new and severe vertebral fractures: results from the HORIZON Pivotal Fracture Trial. Osteoporos Int 23:53\u201358","journal-title":"Osteoporos Int"},{"key":"12393_CR6","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1001\/jama.285.3.320","volume":"285","author":"R Lindsay","year":"2001","unstructured":"Lindsay R, Silverman SL, Cooper C et al (2001) Risk of new vertebral fracture in the year following a fracture. JAMA 285:320\u2013323","journal-title":"JAMA"},{"key":"12393_CR7","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1007\/s00198-007-0413-1","volume":"18","author":"C Roux","year":"2007","unstructured":"Roux C, Fechtenbaum J, Kolta S, Briot K, Girard M (2007) Mild prevalent and incident vertebral fractures are risk factors for new fractures. Osteoporos Int 18:1617\u20131624","journal-title":"Osteoporos Int"},{"key":"12393_CR8","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s00198-013-2460-0","volume":"25","author":"H Johansson","year":"2014","unstructured":"Johansson H, Od\u00e9n A, McCloskey EV, Kanis JA (2014) Mild morphometric vertebral fractures predict vertebral fractures but not non-vertebral fractures. Osteoporos Int 25:235\u2013241","journal-title":"Osteoporos Int"},{"key":"12393_CR9","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1359\/jbmr.1999.14.5.821","volume":"14","author":"DM Black","year":"1999","unstructured":"Black DM, Arden NK, Palermo L, Pearson J, Cummings SR (1999) Prevalent vertebral deformities predict hip fractures and new vertebral deformities but not wrist fractures. Study of Osteoporotic Fractures Research Group. J Bone Miner Res 14:821\u2013828","journal-title":"J Bone Miner Res"},{"key":"12393_CR10","first-page":"92","volume":"43","author":"KN Tu","year":"2018","unstructured":"Tu KN, Lie JD, Wan CKV et al (2018) Osteoporosis: a review of treatment options. P T 43:92\u2013104","journal-title":"P T"},{"key":"12393_CR11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1148\/radiol.12110462","volume":"263","author":"TM Link","year":"2012","unstructured":"Link TM (2012) Osteoporosis imaging: state of the art and advanced imaging. Radiology 263:3\u201317","journal-title":"Radiology"},{"key":"12393_CR12","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s00198-021-06169-x","volume":"33","author":"MT L\u00f6ffler","year":"2022","unstructured":"L\u00f6ffler MT, Kallweit M, Niederreiter E et al (2022) Epidemiology and reporting of osteoporotic vertebral fractures in patients with long-term hospital records based on routine clinical CT imaging. Osteoporos Int 33:685\u2013694","journal-title":"Osteoporos Int"},{"key":"12393_CR13","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.ejrad.2007.08.028","volume":"69","author":"Williams AL","year":"2009","unstructured":"Williams AL, Al-Busaidi A, Sparrow PJ, Adams JE, Whitehouse RW (2009) Under-reporting of osteoporotic vertebral fractures on computed tomography. Eur J Radiol 69:179\u2013183","journal-title":"Eur J Radiol"},{"key":"12393_CR14","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1007\/s00330-008-0920-2","volume":"18","author":"D M\u00fcller","year":"2008","unstructured":"M\u00fcller D, Bauer JS, Zeile M, Rummeny EJ, Link TM (2008) Significance of sagittal reformations in routine thoracic and abdominal multislice CT studies for detecting osteoporotic fractures and other spine abnormalities. Eur Radiol 18:1696\u20131702","journal-title":"Eur Radiol"},{"key":"12393_CR15","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444","journal-title":"Nature"},{"key":"12393_CR16","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"12393_CR17","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25:44\u201356","journal-title":"Nat Med"},{"key":"12393_CR18","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1002\/jbmr.5650080915","volume":"8","author":"HK Genant","year":"1993","unstructured":"Genant HK, Wu CY, van Kuijk C, Nevitt MC (1993) Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res 8:1137\u20131148","journal-title":"J Bone Miner Res"},{"key":"12393_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-021-01060-0","volume":"8","author":"H Liebl","year":"2021","unstructured":"Liebl H, Schinz D, Sekuboyina A et al (2021) A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data. Sci Data 8:284","journal-title":"Sci Data"},{"key":"12393_CR20","doi-asserted-by":"publisher","first-page":"e190138","DOI":"10.1148\/ryai.2020190138","volume":"2","author":"MT L\u00f6ffler","year":"2020","unstructured":"L\u00f6ffler MT, Sekuboyina A, Jacob A et al (2020) A vertebral segmentation dataset with fracture grading. Radiol Artif Intell 2:e190138","journal-title":"Radiol Artif Intell"},{"key":"12393_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102166","volume":"73","author":"A Sekuboyina","year":"2021","unstructured":"Sekuboyina A, Husseini ME, Bayat A et al (2021) VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images. Med Image Anal 73:102166","journal-title":"Med Image Anal"},{"key":"12393_CR22","doi-asserted-by":"crossref","unstructured":"World Medical Association (2013) World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310:2191\u20132194","DOI":"10.1001\/jama.2013.281053"},{"key":"12393_CR23","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116\u20131128","journal-title":"Neuroimage"},{"key":"12393_CR24","doi-asserted-by":"crossref","unstructured":"Engstler P, Keicher M, Schinz D et al (2022) Interpretable vertebral fracture diagnosis. In: Reyes M, Henriques Abreu P, Cardoso J (eds) Interpretability of machine intelligence in medical image computing. Springer, Cham, pp 71\u201381","DOI":"10.1007\/978-3-031-17976-1_7"},{"key":"12393_CR25","unstructured":"Buess L, Stollenga MF, Schinz D et al (2024) Video-CT MAE: self-supervised video-CT domain adaptation for vertebral fracture diagnosis. PMLR 250:151\u2013167"},{"key":"12393_CR26","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the 2017 IEEE conference on computer vision and pattern recognition. IEEE, New York, NY, USA, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"12393_CR27","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the 2018 IEEE conference on computer vision and pattern recognition. IEEE, New York, NY, USA, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"12393_CR28","doi-asserted-by":"publisher","first-page":"276","DOI":"10.11613\/BM.2012.031","volume":"22","author":"ML McHugh","year":"2012","unstructured":"McHugh ML (2012) Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 22:276\u2013282","journal-title":"Biochem Med"},{"key":"12393_CR29","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1007\/s00198-017-3909-3","volume":"28","author":"G Ballane","year":"2017","unstructured":"Ballane G, Cauley JA, Luckey MM, El-Hajj Fuleihan G (2017) Worldwide prevalence and incidence of osteoporotic vertebral fractures. Osteoporos Int 28:1531\u20131542","journal-title":"Osteoporos Int"},{"key":"12393_CR30","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1359\/jbmr.2002.17.6.1051","volume":"17","author":"M Van der Klift","year":"2002","unstructured":"Van der Klift M, De Laet CE, McCloskey EV, Hofman A, Pols HA (2002) The incidence of vertebral fractures in men and women: the Rotterdam study. J Bone Miner Res 17:1051\u20131056","journal-title":"J Bone Miner Res"},{"key":"12393_CR31","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1359\/jbmr.2002.17.4.716","volume":"17","author":"D Felsenberg","year":"2002","unstructured":"Felsenberg D, Silman AJ, Lunt M et al (2002) Incidence of vertebral fracture in Europe: results from the European Prospective Osteoporosis Study (EPOS). J Bone Miner Res 17:716\u2013724","journal-title":"J Bone Miner Res"},{"key":"12393_CR32","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1002\/jbmr.4916","volume":"38","author":"J Nicolaes","year":"2023","unstructured":"Nicolaes J, Skj\u00f8dt MK, Raeymaeckers S et al (2023) Towards improved identification of vertebral fractures in routine computed tomography (CT) scans: development and external validation of a machine learning algorithm. J Bone Miner Res 38:1856\u20131866","journal-title":"J Bone Miner Res"},{"key":"12393_CR33","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1148\/radiol.2017162100","volume":"284","author":"JE Burns","year":"2017","unstructured":"Burns JE, Yao J, Summers RM (2017) Vertebral body compression fractures and bone density: automated detection and classification on CT images. Radiology 284:788\u2013797","journal-title":"Radiology"},{"key":"12393_CR34","unstructured":"GBD 2019 Dementia Forecasting Collaborators (2022) Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health 7:e105\u2013e125"},{"key":"12393_CR35","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230614","volume":"310","author":"Y Kim","year":"2024","unstructured":"Kim Y, Kim YG, Park JW et al (2024) A CT-based deep learning model for predicting subsequent fracture risk in patients with hip fracture. Radiology 310:e230614","journal-title":"Radiology"},{"key":"12393_CR36","doi-asserted-by":"publisher","unstructured":"Bar A, Wolf L, Bergman Amitai O, Toledano E, Elnekave E (2017) Compression fractures detection on CT. In: Medical Imaging 2017: Computer-Aided Diagnosis. SPIE, Bellingham, WA, USA, Proc SPIE 10134:1013440. https:\/\/doi.org\/10.1117\/12.2249635","DOI":"10.1117\/12.2249635"},{"key":"12393_CR37","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.compbiomed.2018.05.011","volume":"98","author":"N Tomita","year":"2018","unstructured":"Tomita N, Cheung YY, Hassanpour S (2018) Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med 98:8\u201315","journal-title":"Comput Biol Med"},{"key":"12393_CR38","doi-asserted-by":"crossref","unstructured":"Yilmaz EB, Buerger C, Fricke T et al (2021) Automated deep learning-based detection of osteoporotic fractures in CT images. In: Lian C, Cao X, Rekik I, Xu X, Yan P (eds) Machine learning in medical imaging. Springer, Cham, pp 376\u2013385","DOI":"10.1007\/978-3-030-87589-3_39"},{"key":"12393_CR39","doi-asserted-by":"publisher","DOI":"10.1002\/jbm4.10778","volume":"7","author":"JH Page","year":"2023","unstructured":"Page JH, Moser FG, Maya MM, Prasad R, Pressman BD (2023) Opportunistic CT screening-machine learning algorithm identifies majority of vertebral compression fractures: a cohort study. JBMR Plus 7:e10778","journal-title":"JBMR Plus"},{"key":"12393_CR40","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1007\/s00223-024-01196-2","volume":"114","author":"MG Bendtsen","year":"2024","unstructured":"Bendtsen MG, Hitz MF (2024) Opportunistic identification of vertebral compression fractures on CT scans of the chest and abdomen, using an AI algorithm, in a real-life setting. Calcif Tissue Int 114:468\u2013479","journal-title":"Calcif Tissue Int"}],"updated-by":[{"DOI":"10.1007\/s00330-026-12637-x","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000}}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12393-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-026-12393-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12393-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T09:53:30Z","timestamp":1789379610000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-026-12393-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,24]]},"references-count":40,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["12393"],"URL":"https:\/\/doi.org\/10.1007\/s00330-026-12393-y","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,24]]},"assertion":[{"value":"4 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2026","order":6,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":7,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised: Modifications have been made to the graphical abstract, tables 1, 2, 3 and 4, figures 3, 4, 5 and 6, and to the text. Full information regarding the corrections made can be found in the erratum\/correction for this article.","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2026","order":9,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":10,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":11,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00330-026-12637-x","URL":"https:\/\/doi.org\/10.1007\/s00330-026-12637-x","order":12,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The scientific guarantor of this publication is Jan S. Kirschke.","order":1,"name":"Ethics","label":"Guarantor","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Jan S. Kirschke, Sebastian R\u00fchling, and Malek El Husseini are co-founders of Bonescreen. These authors had no influence on the statistical analysis, results calculation, and interpretation. All other authors declare no conflicts of interest.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"No complex statistical methods exceeding the statistical expertise of the authors were necessary for this paper.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The internal dataset used for training was de-identified in compliance with the Health Insurance Portability and Accountability Act (HIPAA). The institutional review board (Ethikkommission der Technischen Universit\u00e4t M\u00fcnchen) waived informed consent (Approval No. [27\/19 S-SR]). No separate study protocol was registered. The study complies with the ethical standards of the 1964 Declaration of Helsinki and its later amendments.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The internal dataset used for training was de-identified in compliance with the Health Insurance Portability and Accountability Act (HIPAA). The institutional review board (Ethikkommission der Technischen Universit\u00e4t M\u00fcnchen) waived informed consent (Approval No. [27\/19 S-SR]). No separate study protocol was registered. The study complies with the ethical standards of the 1964 Declaration of Helsinki and its later amendments.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The data analyzed stem from the publicly available VerSe 19 & 20 datasets, as referenced\u00a0above.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}