{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:31:12Z","timestamp":1787027472010,"version":"build-2736575974"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Automated lumbar computed tomography (CT) Hounsfield unit (HU) measurement can support opportunistic osteoporosis screening, but heterogeneous Digital Imaging and Communications in Medicine (DICOM) inputs often disrupt automated workflows before measurement. We evaluated a local Qwen2.5-VL-7B vision-language model (VLM)-assisted front end for suitability routing, input planning, and HU-traceable DICOM-derived field-of-view\/orientation adaptation upstream of an unchanged single-slice lumbar CT HU workflow. The VLM was restricted to routing and planning. In a 20-case FUJIFILM pilot cohort, observed HU output availability was higher with the front-end-assisted route than with direct processing (12\/20 versus 5\/20), while paired automatic-success cases showed excellent agreement with post hoc manual ImageJ (version 1.54g) measurements (ICC(A,1) = 0.997; MAE = 4.64 HU). Public DICOM stress testing further demonstrated improved workflow robustness across heterogeneous datasets. These findings support the use of a deployment-oriented front-end strategy to improve the auditability and robustness of automated lumbar CT HU measurement while preserving an unchanged downstream measurement workflow.<\/jats:p>","DOI":"10.3390\/jimaging12080385","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T15:48:36Z","timestamp":1786722516000},"page":"385","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VLM-Assisted Routing and HU-Traceable DICOM Adaptation for Lumbar CT HU Measurement: A Deployment-Oriented Pilot Technical Evaluation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4228-941X","authenticated-orcid":false,"given":"Zhe-Yu","family":"Ye","sequence":"first","affiliation":[{"name":"Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-Mu","family":"Peng","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7293-7471","authenticated-orcid":false,"given":"Tamotsu","family":"Kamishima","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"588","DOI":"10.7326\/0003-4819-158-8-201304160-00003","article-title":"Opportunistic Screening for Osteoporosis Using Abdominal Computed Tomography Scans Obtained for Other Indications","volume":"158","author":"Pickhardt","year":"2013","journal-title":"Ann. Intern. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2194","DOI":"10.1002\/jbmr.428","article-title":"Simultaneous Screening for Osteoporosis at CT Colonography: Bone Mineral Density Assessment Using MDCT Attenuation Techniques Compared with the DXA Reference Standard","volume":"26","author":"Pickhardt","year":"2011","journal-title":"J. Bone Miner. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1148\/radiol.2019181648","article-title":"Opportunistic Osteoporosis Screening at Routine Abdominal and Thoracic CT: Normative L1 Trabecular Attenuation Values in More than 20,000 Adults","volume":"291","author":"Jang","year":"2019","journal-title":"Radiology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.2106\/JBJS.J.00160","article-title":"Hounsfield Units for Assessing Bone Mineral Density and Strength: A Tool for Osteoporosis Management","volume":"93","author":"Schreiber","year":"2011","journal-title":"J. Bone Jt. Surg. Am. Vol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1002\/jbmr.3383","article-title":"Future Osteoporotic Fracture Risk Related to Lumbar Vertebral Trabecular Attenuation Measured at Routine Body CT","volume":"33","author":"Lee","year":"2018","journal-title":"J. Bone Miner. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"E239","DOI":"10.1097\/BRS.0000000000002813","article-title":"Measurement Techniques and Utility of Hounsfield Unit Values for Assessment of Bone Quality Prior to Spinal Instrumentation: A Review of Current Literature","volume":"44","author":"Zaidi","year":"2019","journal-title":"Spine"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jocd.2007.12.010","article-title":"Clinical Use of Quantitative Computed Tomography and Peripheral Quantitative Computed Tomography in the Management of Osteoporosis in Adults: The 2007 ISCD Official Positions","volume":"11","author":"Engelke","year":"2008","journal-title":"J. Clin. Densitom."},{"key":"ref_8","unstructured":"American College of Radiology (2023). ACR\u2013SPR\u2013SSR Practice Parameter for the Performance of Quantitative Computed Tomography (QCT) Bone Mineral Density, American College of Radiology."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1007\/s00330-013-3089-2","article-title":"Automatic Detection of Osteoporotic Vertebral Fractures in Routine Thoracic and Abdominal MDCT","volume":"24","author":"Baum","year":"2014","journal-title":"Eur. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102166","DOI":"10.1016\/j.media.2021.102166","article-title":"VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-Detector CT Images","volume":"73","author":"Sekuboyina","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e230024","DOI":"10.1148\/ryai.230024","article-title":"TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images","volume":"5","author":"Wasserthal","year":"2023","journal-title":"Radiol. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ye, Z.-Y., Peng, J.-M., Lu, B.-Q., and Kamishima, T. (2026). Automated Single-Slice Lumbar QCT HU Value Measurement with Clinical Workflow. Mach. Learn. Knowl. Extr., 8.","DOI":"10.3390\/make8030077"},{"key":"ref_15","unstructured":"National Electrical Manufacturers Association (2026). Digital Imaging and Communications in Medicine (DICOM) Standard, PS3.3: Information Object Definitions, NEMA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository","volume":"26","author":"Clark","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Prior, F.W., Clark, K., Commean, P., Freymann, J., Jaffe, C., Kirby, J., Moore, S., Smith, K., Tarbox, L., and Vendt, B. (2013, January 3\u20137). TCIA: An Information Resource to Enable Open Science. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6609742"},{"key":"ref_18","unstructured":"National Cancer Institute (2026, May 26). National Biomedical Imaging Archive (NBIA) Data Retriever and REST API Documentation, Available online: https:\/\/wiki.cancerimagingarchive.net\/."},{"key":"ref_19","unstructured":"Simpson, A.L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., van Ginneken, B., Kopp-Schneider, A., Landman, B.A., Litjens, G., and Menze, B. (2019). A Large Annotated Medical Image Dataset for the Development and Evaluation of Segmentation Algorithms. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e200029","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"Reporting Guidelines for Clinical Trial Reports for Interventions Involving Artificial Intelligence: The CONSORT-AI Extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1038\/s41591-020-1037-7","article-title":"Guidelines for Clinical Trial Protocols for Interventions Involving Artificial Intelligence: The SPIRIT-AI Extension","volume":"26","author":"Rivera","year":"2020","journal-title":"Nat. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting Guideline for the Early Stage Clinical Evaluation of Decision Support Systems Driven by Artificial Intelligence: DECIDE-AI","volume":"28","author":"Vasey","year":"2022","journal-title":"Nat. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e048008","DOI":"10.1136\/bmjopen-2020-048008","article-title":"Protocol for Development of a Reporting Guideline (TRIPOD-AI) and Risk of Bias Tool (PROBAST-AI) for Diagnostic and Prognostic Prediction Model Studies Based on Artificial Intelligence","volume":"11","author":"Collins","year":"2021","journal-title":"BMJ Open"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., and Oermann, E.K. (2018). Variable Generalization Performance of a Deep Learning Model to Detect Pneumonia in Chest Radiographs: A Cross-Sectional Study. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002683"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-Performance Medicine: The Convergence of Human and Artificial Intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation Models for Generalist Medical Artificial Intelligence","volume":"616","author":"Moor","year":"2023","journal-title":"Nature"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large Language Models Encode Clinical Knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"ref_30","unstructured":"Bai, S., Chen, K., Liu, X., Wang, J., Ge, W., Song, S., Dang, K., Wang, P., Wang, S., and Tang, J. (2025). Qwen2.5-VL Technical Report. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1038\/nmeth.2089","article-title":"NIH Image to ImageJ: 25 Years of Image Analysis","volume":"9","author":"Schneider","year":"2012","journal-title":"Nat. Methods"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research","volume":"15","author":"Koo","year":"2016","journal-title":"J. Chiropr. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","article-title":"Statistical Methods for Assessing Agreement between Two Methods of Clinical Measurement","volume":"1","author":"Bland","year":"1986","journal-title":"Lancet"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/8\/385\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:22:20Z","timestamp":1787026940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/8\/385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,14]]},"references-count":33,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["jimaging12080385"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12080385","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,14]]}}}