{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:46:55Z","timestamp":1792176415052,"version":"4.4.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100019180","name":"HORIZON EUROPE European Research Council","doi-asserted-by":"publisher","award":["101045128 \u2013 iBack-epic \u2013 ERC2021-COG"],"award-info":[{"award-number":["101045128 \u2013 iBack-epic \u2013 ERC2021-COG"]}],"id":[{"id":"10.13039\/100019180","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>Although the identification of enumeration anomalies has potential clinical implications for chronic back pain and operation planning, the thoracolumbar junction is poorly assessed and rarely described in clinical reports. Additionally, even though multiple deep-learning-based vertebra labeling algorithms exist, there is a lack of methods to automatically label enumeration anomalies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>We introduce \u201cVertebra Identification with Anomaly Handling\u201d (VERIDAH), a novel vertebra labeling algorithm based on multiple classification heads combined with a weighted vertebra sequence prediction algorithm. We use the NAKO dataset for MRI and a CT cohort to retrospectively evaluate the labeling performance by computing how often the model is correct in predicting the vertebra labels, including enumeration anomalies. Statistical significance was assessed using the Wilcoxon signed-rank test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      On 1000 T2w sagittal (mean age 52\u2009\u00b1\u200911, 52% female) and 365 CT (mean age 74\u2009\u00b1\u200921, 44% female) images, we show that our approach surpasses existing models on T2w TSE sagittal (98.30% vs. 94.24% of subjects with all vertebrae correctly labeled,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01) and CT imaging (99.18% vs. 77.26% of subjects with all vertebrae correctly labeled,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01) and works in arbitrary field-of-view images. VERIDAH correctly labeled the presence of thoracic enumeration anomalies in 87.80% and 96.30% of T2w and CT images, respectively, and lumbar enumeration anomalies in 94.48% and 97.22% for T2w and CT, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>VERIDAH offers enumeration anomaly robust vertebra labeling in CT and T2w sagittal imaging and is publicly released along with a spine segmentation model.<\/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>Automatically identifying enumeration anomalies in the spine is important in determining the correct vertebra labels, especially in limited field-of-view imaging<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>A carefully designed classifier with multiple classification heads and a sequence predictor provides superior results on large benchmarks, both in CT and MRI<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The proposed method identifies vertebra levels even in very limited field-of-view imaging, and correctly predicts enumeration anomalies, making downstream tasks such as surgery planning easier. Its public availability enables studies and applications to take these spinal anomalies into account<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12966-x","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:58:20Z","timestamp":1792173500000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VERIDAH: solving enumeration anomaly\u2013aware vertebra labeling across imaging sequences"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1978-5894","authenticated-orcid":false,"given":"Hendrik","family":"M\u00f6ller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Sch\u00f6n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Graf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matan","family":"Atad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan","family":"Molinier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anjany","family":"Sekuboyina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bettina K.","family":"Budai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabian","family":"Bamberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"Ringhof","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Schlett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Pischon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thoralf","family":"Niendorf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josua A.","family":"Decker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc-Andr\u00e9","family":"Weber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bjoern","family":"Menze","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Rueckert","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":[[2026,10,16]]},"reference":[{"key":"12966_CR1","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1148\/radiol.2019182465","volume":"294","author":"Y Sim","year":"2020","unstructured":"Sim Y, Chung MJ, Kotter E et al (2020) Deep convolutional neural network\u2013based software improves radiologist detection of malignant lung nodules on chest radiographs. Radiology 294:199\u2013209","journal-title":"Radiology"},{"key":"12966_CR2","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1016\/S1470-2045(19)30098-1","volume":"20","author":"P Kickingereder","year":"2019","unstructured":"Kickingereder P, Isensee F, Tursunova I et al (2019) Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 20:728\u2013740","journal-title":"Lancet Oncol"},{"key":"12966_CR3","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw J, Ledsam JR, Romera-Paredes B et al (2018) Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 24:1342\u20131350","journal-title":"Nat Med"},{"key":"12966_CR4","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":"12966_CR5","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.rcl.2012.04.003","volume":"50","author":"GK Thawait","year":"2012","unstructured":"Thawait GK, Chhabra A, Carrino JA (2012) Spine segmentation and enumeration and normal variants. Radiol Clin North Am 50:587\u2013598","journal-title":"Radiol Clin North Am"},{"key":"12966_CR6","doi-asserted-by":"publisher","DOI":"10.2106\/JBJS.OA.20.00167","volume":"6","author":"Y Tatara","year":"2021","unstructured":"Tatara Y, Niimura T, Sekiya T, Mihara H (2021) Changes in lumbosacral anatomy and vertebral numbering in patients with thoracolumbar and\/or lumbosacral transitional vertebrae. JBJS Open Access 6:e20","journal-title":"JBJS Open Access"},{"key":"12966_CR7","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1097\/00007632-198005000-00003","volume":"5","author":"RE Wigh","year":"1980","unstructured":"Wigh RE (1980) The thoracolumbar and lumbosacral transitional junctions. Spine 5:215\u2013222","journal-title":"Spine"},{"key":"12966_CR8","doi-asserted-by":"publisher","first-page":"20150784","DOI":"10.1259\/bjr.20150784","volume":"89","author":"SK Park","year":"2016","unstructured":"Park SK, Park JG, Kim BS, Huh JD, Kang H (2016) Thoracolumbar junction: morphologic characteristics, various variants and significance. Br J Radiol 89:20150784","journal-title":"Br J Radiol"},{"key":"12966_CR9","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1148\/radiol.12112747","volume":"265","author":"L Nardo","year":"2012","unstructured":"Nardo L, Alizai H, Virayavanich W et al (2012) Lumbosacral transitional vertebrae: association with low back pain. Radiology 265:497\u2013503","journal-title":"Radiology"},{"key":"12966_CR10","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s11547-018-0974-4","volume":"124","author":"M Apaydin","year":"2019","unstructured":"Apaydin M, Uluc ME, Sezgin G (2019) Lumbosacral transitional vertebra in the young men population with low back pain: anatomical considerations and degenerations (transitional vertebra types in the young men population with low back pain). Radiol Med 124:375\u2013381","journal-title":"Radiol Med"},{"key":"12966_CR11","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1302\/0301-620X.103B7.BJJ-2020-1760.R1","volume":"103-B","author":"K Sugiura","year":"2021","unstructured":"Sugiura K, Morimoto M, Higashino K et al (2021) Transitional vertebrae and numerical variants of the spine: prevalence and relationship to low back pain or degenerative spondylolisthesis. Bone Joint J 103-B:1301\u20131308. https:\/\/doi.org\/10.1302\/0301-620X.103B7.BJJ-2020-1760.R1","journal-title":"Bone Joint J"},{"key":"12966_CR12","doi-asserted-by":"publisher","first-page":"467","DOI":"10.3171\/SPI-07\/11\/467","volume":"7","author":"BS Jhawar","year":"2007","unstructured":"Jhawar BS, Mitsis D, Duggal N (2007) Wrong-sided and wrong-level neurosurgery: a national survey. J Neurosurg Spine 7:467\u2013472. https:\/\/doi.org\/10.3171\/SPI-07\/11\/467","journal-title":"J Neurosurg Spine"},{"key":"12966_CR13","doi-asserted-by":"crossref","unstructured":"Hahn PY, Strobel JJ, Hahn FJ (1992) Verification of lumbosacral segments on MR images: identification of transitional vertebrae. Radiology 182:580\u2013581","DOI":"10.1148\/radiology.182.2.1732988"},{"key":"12966_CR14","doi-asserted-by":"crossref","unstructured":"Peckham ME, Hutchins TA, Stilwill SE et al (2017) Localizing the L5 vertebra using nerve morphology on MRI: an accurate and reliable technique. AJNR Am J Neuroradiol 38:2008\u20132014","DOI":"10.3174\/ajnr.A5311"},{"key":"12966_CR15","doi-asserted-by":"publisher","first-page":"2195","DOI":"10.1007\/s00586-025-09364-0","volume":"35","author":"D Schinz","year":"2026","unstructured":"Schinz D, M\u00f6ller H, Ramsch\u00fctz C et al (2026) A vertebral shape-based classification for labeling the thoracolumbar junction on CT imaging. Eur Spine J 35:2195\u20132203. https:\/\/doi.org\/10.1007\/s00586-025-09364-0","journal-title":"Eur Spine J"},{"key":"12966_CR16","doi-asserted-by":"publisher","first-page":"1676","DOI":"10.1109\/TMI.2015.2392054","volume":"34","author":"Y Cai","year":"2015","unstructured":"Cai Y, Osman S, Sharma M, Landis M, Li S (2015) Multi-modality vertebra recognition in arbitrary views using 3D deformable hierarchical model. IEEE Trans Med Imaging 34:1676\u20131693","journal-title":"IEEE Trans Med Imaging"},{"key":"12966_CR17","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.1109\/TMI.2018.2798293","volume":"37","author":"H Liao","year":"2018","unstructured":"Liao H, Mesfin A, Luo J (2018) Joint vertebrae identification and localization in spinal CT images by combining short-and long-range contextual information. IEEE Trans Med Imaging 37:1266\u20131275","journal-title":"IEEE Trans Med Imaging"},{"key":"12966_CR18","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.media.2019.03.007","volume":"54","author":"C Payer","year":"2019","unstructured":"Payer C, \u0160tern D, Bischof H, Urschler M (2019) Integrating spatial configuration into heatmap regression based CNNs for landmark localization. Med Image Anal 54:207\u2013219","journal-title":"Med Image Anal"},{"key":"12966_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMI.2010.2047403","volume":"30","author":"RS Alomari","year":"2010","unstructured":"Alomari RS, Corso JJ, Chaudhary V (2010) Labeling of lumbar discs using both pixel-and object-level features with a two-level probabilistic model. IEEE Trans Med Imaging 30:1\u201310","journal-title":"IEEE Trans Med Imaging"},{"key":"12966_CR20","doi-asserted-by":"publisher","first-page":"102166","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":"12966_CR21","doi-asserted-by":"publisher","first-page":"102258","DOI":"10.1016\/j.media.2021.102258","volume":"75","author":"R Tao","year":"2022","unstructured":"Tao R, Liu W, Zheng G (2022) Spine-transformers: vertebra labeling and segmentation in arbitrary field-of-view spine CTs via 3D transformers. Med Image Anal 75:102258","journal-title":"Med Image Anal"},{"key":"12966_CR22","doi-asserted-by":"publisher","first-page":"125020","DOI":"10.1088\/1361-6560\/ac07c7","volume":"66","author":"Y Huang","year":"2021","unstructured":"Huang Y, Uneri A, Jones CK et al (2021) 3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework. Phys Med Biol 66:125020","journal-title":"Phys Med Biol"},{"key":"12966_CR23","first-page":"15330","volume":"12","author":"ID Kawathekar","year":"2024","unstructured":"Kawathekar ID, Areeckal AS, Aparna V (2024) A novel deep learning pipeline for vertebra labeling and segmentation of spinal computed tomography images. IEEE Access Pract Innov Open Solut 12:15330\u201315347","journal-title":"IEEE Access Pract Innov Open Solut"},{"key":"12966_CR24","doi-asserted-by":"crossref","unstructured":"Windsor R, Jamaludin A, Kadir T, Zisserman A (2024) Automated detection, labelling and radiological grading of clinical spinal MRIs. Sci Rep 14:14993","DOI":"10.1038\/s41598-024-64580-w"},{"key":"12966_CR25","doi-asserted-by":"crossref","unstructured":"Sekuboyina A, Irmai J, Shit S, Kirschke JS, Andres B, Menze B (2023) Pushing the limits of an FCN and a CRF towards near-ideal vertebrae labelling. In: Proceedings of the 2023 IEEE 20th international symposium on biomedical imaging. IEEE, Cartagena, pp 1\u20135","DOI":"10.1109\/ISBI53787.2023.10230520"},{"key":"12966_CR26","doi-asserted-by":"crossref","unstructured":"Meng D, Mohammed E, Boyer E, Pujades S (2022) Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle. In: International workshop on machine learning in medical imaging. Springer, Cham, pp 307\u2013317","DOI":"10.1007\/978-3-031-21014-3_32"},{"key":"12966_CR27","doi-asserted-by":"publisher","unstructured":"Warszawer Y, Molinier N, Valo\u0161ek J et al (2024) Fully automatic vertebrae and spinal cord segmentation using a hybrid approach combining nnU-Net and iterative algorithm. In: Proceedings of the International Society for Magnetic Resonance in Medicine, scientific meeting and exhibition. https:\/\/doi.org\/10.58530\/2024\/4847","DOI":"10.58530\/2024\/4847"},{"key":"12966_CR28","doi-asserted-by":"publisher","first-page":"587","DOI":"10.3238\/arztebl.m2024.0151","volume":"121","author":"F Bamberg","year":"2024","unstructured":"Bamberg F, Schlett CL, Caspers S et al (2024) Baseline MRI examination in the NAKO health study. Dtsch Arztebl Int 121:587\u2013593. https:\/\/doi.org\/10.3238\/arztebl.m2024.0151","journal-title":"Dtsch Arztebl Int"},{"key":"12966_CR29","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1148\/radiol.2015142272","volume":"277","author":"F Bamberg","year":"2015","unstructured":"Bamberg F, Kauczor H-U, Weckbach S et al (2015) Whole-body MR imaging in the German National Cohort: rationale, design, and technical background. Radiology 277:206\u2013220","journal-title":"Radiology"},{"key":"12966_CR30","doi-asserted-by":"publisher","unstructured":"M\u00f6ller H, Graf R, Schmitt J et al (2024) SPINEPS\u2014automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-024-11155-y","DOI":"10.1007\/s00330-024-11155-y"},{"key":"12966_CR31","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.3174\/ajnr.A2036","volume":"31","author":"GP Konin","year":"2010","unstructured":"Konin GP, Walz DM (2010) Lumbosacral transitional vertebrae: classification, imaging findings, and clinical relevance. AJNR Am J Neuroradiol 31:1778\u20131786","journal-title":"AJNR Am J Neuroradiol"},{"key":"12966_CR32","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Honolulu, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"12966_CR33","doi-asserted-by":"crossref","unstructured":"Imambi S, Prakash KB, Kanagachidambaresan GR (2021) PyTorch. Programming with TensorFlow. In: Solution for Edge Computing Applications. Springer, pp 87\u2013104","DOI":"10.1007\/978-3-030-57077-4_10"},{"key":"12966_CR34","doi-asserted-by":"publisher","unstructured":"Falcon W, The PyTorch Lightning team (2019) PyTorch Lightning. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.22687155","DOI":"10.5281\/zenodo.22687155"},{"key":"12966_CR35","doi-asserted-by":"publisher","unstructured":"Cardoso MJ, Li W, Brown R et al (2022) Monai: an open-source framework for deep learning in healthcare. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2211.02701","DOI":"10.48550\/arXiv.2211.02701"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12966-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-026-12966-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12966-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:58:22Z","timestamp":1792173502000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-026-12966-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,16]]},"references-count":35,"alternative-id":["12966"],"URL":"https:\/\/doi.org\/10.1007\/s00330-026-12966-x","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,16]]},"assertion":[{"value":"11 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The scientific guarantor of this publication is Hendrik M\u00f6ller.","order":1,"name":"Ethics","label":"Guarantor","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors of this manuscript declare relationships with the following companies: J.S.K. and A.S. are co-founders and shareholders of Bonescreen GmbH (see\n                      \n                      ). During this study, the authors who analyzed and controlled the data are not employees, co-founders, or shareholders of Bonescreen GmbH. All remaining authors declare to have no competing interests.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"No complex statistical methods were necessary for this paper.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was obtained (593\/21 S-NP).","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"There is no subject overlap with internal data. We utilize a subset of the German National Cohort (NAKO). This article has a preprint: M\u00f6ller H, Schoen H, Graf R et al (2026) \u201cVERIDAH: solving enumeration anomaly aware vertebra labeling across imaging sequences\u201d.\n                      \n                      (arXiv).","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"}}]}}