{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:07:23Z","timestamp":1784563643925,"version":"3.55.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100017695","name":"Health Commission of Hunan Province","doi-asserted-by":"publisher","award":["20220407968"],"award-info":[{"award-number":["20220407968"]}],"id":[{"id":"10.13039\/100017695","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02123-7","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T15:41:29Z","timestamp":1784562089000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An ROI-Guided Imaging Informatics Framework for Image-Language Differentiation of Tuberculous and Pyogenic Spondylitis on MRI"],"prefix":"10.1007","author":[{"given":"Xuming","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binqing","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuerong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiliang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lincong","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6880-7800","authenticated-orcid":false,"given":"Xiaotao","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"key":"2123_CR1","doi-asserted-by":"publisher","unstructured":"Cromb\u00e9 A, Fadli D, Clinca R et al (2024) Imaging of spondylodiscitis: a comprehensive updated review-multimodality imaging findings, differential diagnosis, and specific microorganisms detection. Microorganisms 12:893. https:\/\/doi.org\/10.3390\/microorganisms12050893","DOI":"10.3390\/microorganisms12050893"},{"key":"2123_CR2","doi-asserted-by":"publisher","unstructured":"Yu D, Kang Y, Lu W, Chen B (2025) Progress in diagnosis and treatment of primary spondylodiscitis: a systematic literature review. EFORT Open Rev 10:815\u2013828. https:\/\/doi.org\/10.1530\/EOR-2025-0041","DOI":"10.1530\/EOR-2025-0041"},{"key":"2123_CR3","doi-asserted-by":"publisher","unstructured":"Yeo JJY, Wong SBS, Yeap PM (2025) Revisiting imaging features of spinal tuberculosis. Singapore Med J 66:576\u2013581. https:\/\/doi.org\/10.4103\/singaporemedj.SMJ-2022-214","DOI":"10.4103\/singaporemedj.SMJ-2022-214"},{"key":"2123_CR4","doi-asserted-by":"publisher","unstructured":"Conti J, Geremia N, Di Bella S et al (2025) Infectious spondylodiscitis: epidemiology, diagnosis, microbiological findings, clinical features and outcomes in a 14-year retrospective study. Eur J Clin Microbiol Infect Dis 44:3067\u20133078. https:\/\/doi.org\/10.1007\/s10096-025-05292-5","DOI":"10.1007\/s10096-025-05292-5"},{"key":"2123_CR5","doi-asserted-by":"publisher","unstructured":"Jiang G, Sun L-L, Yang Z-T, Cui J-F, Zhang Q-Y, Gao C-P (2024) Primary spinal epidural abscess: magnetic resonance imaging characteristics and diagnosis. BMC Med Imaging 24:278. https:\/\/doi.org\/10.1186\/s12880-024-01458-3","DOI":"10.1186\/s12880-024-01458-3"},{"key":"2123_CR6","doi-asserted-by":"publisher","unstructured":"Schulz E, B\u00e4ttig L, Stengel FC et al (2025) Management and outcome of whole-spine epidural abscesses - institutional case series and systematic review. Brain Spine 5:104297. https:\/\/doi.org\/10.1016\/j.bas.2025.104297","DOI":"10.1016\/j.bas.2025.104297"},{"key":"2123_CR7","doi-asserted-by":"publisher","unstructured":"Du Q, Kong W, Chang Y et al (2026) Automated detection of cervical spinal cord compression on MRI using YOLO11 deep learning architecture: a two-center external validation study. Spine (Phila Pa 1976) https:\/\/doi.org\/10.1097\/BRS.0000000000005639","DOI":"10.1097\/BRS.0000000000005639"},{"key":"2123_CR8","doi-asserted-by":"publisher","unstructured":"Wang J, Li Z, Chi X et al (2024) Development of a diagnostic model for differentiating tuberculous spondylitis and pyogenic spondylitis with MRI: a multicenter retrospective observational study. Spine 49:34\u201345. https:\/\/doi.org\/10.1097\/BRS.0000000000004848","DOI":"10.1097\/BRS.0000000000004848"},{"key":"2123_CR9","doi-asserted-by":"publisher","unstructured":"Tanaviriyachai T, Pornsopanakorn P, Choovongkomol K et al (2024) A comparativefactor analysis and new magnetic resonance imaging scoring system fordifferentiating pyogenic versus tuberculous spondylodiscitis. Neurospine 21:690 \u2013700. https:\/\/doi.org\/10.14245\/ns.2448120.060","DOI":"10.14245\/ns.2448120.060"},{"key":"2123_CR10","doi-asserted-by":"publisher","unstructured":"Piccolo CL, Villanacci A, Di Stefano F et al (2024) Spondylodiscitis and its mimickers: a pictorial review. Biomedicines 12:2566. https:\/\/doi.org\/10.3390\/biomedicines12112566","DOI":"10.3390\/biomedicines12112566"},{"key":"2123_CR11","doi-asserted-by":"publisher","unstructured":"Compagnone D, Cecchinato R, Pezzi A et al (2023) Diagnostic approach and differences between spinal infections and tumors. Diagnostics (Basel) 13:2737. https:\/\/doi.org\/10.3390\/diagnostics13172737","DOI":"10.3390\/diagnostics13172737"},{"key":"2123_CR12","doi-asserted-by":"publisher","unstructured":"Hirunpat P, Panyaping T, Wongpipathpong W, Hirunpat S (2025) Imaging clues for the diagnosis of various pathogenic causes of infectious spondylitis. Skeletal Radiol 54:2411\u20132424. https:\/\/doi.org\/10.1007\/s00256-025-04943-0","DOI":"10.1007\/s00256-025-04943-0"},{"key":"2123_CR13","doi-asserted-by":"publisher","unstructured":"Yasin P, Yimit Y, Abliz D et al (2024) MRI-based interpretable radiomics nomogram for discrimination between brucella spondylitis and pyogenic spondylitis. Heliyon 10:e23584. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e23584","DOI":"10.1016\/j.heliyon.2023.e23584"},{"key":"2123_CR14","doi-asserted-by":"publisher","unstructured":"\u015eah V, Baran A\u0130 (2024) Can hematological inflammatory indices be used to differentiate Modic type 1 changes from brucella spondylodiscitis? Medicina (Kaunas) 60:1131. https:\/\/doi.org\/10.3390\/medicina60071131","DOI":"10.3390\/medicina60071131"},{"key":"2123_CR15","doi-asserted-by":"publisher","unstructured":"Lang S, Walter N, Heidemanns S et al (2024) [18F]FDG PET\/CT imaging is associated with lower in-hospital mortality in patients with pyogenic spondylodiscitis-a registry-based analysis of 29,362 cases. Antibiotics (Basel) 13:860. https:\/\/doi.org\/10.3390\/antibiotics13090860","DOI":"10.3390\/antibiotics13090860"},{"key":"2123_CR16","doi-asserted-by":"publisher","unstructured":"Zhu W, Zhou S, Zhang J, Li L, Liu P, Xiong W (2024) Differentiation of native vertebral osteomyelitis: a comprehensive review of imaging techniques and future applications. Med Sci Monit 30:e943168. https:\/\/doi.org\/10.12659\/MSM.943168","DOI":"10.12659\/MSM.943168"},{"key":"2123_CR17","doi-asserted-by":"publisher","unstructured":"Xu D, Zhou H, Jie T et al (2025) Data-driven deep learning for predicting ligament fatigue failure risk mechanisms. Int J Mech Sci 301:110519. https:\/\/doi.org\/10.1016\/j.ijmecsci.2025.110519","DOI":"10.1016\/j.ijmecsci.2025.110519"},{"key":"2123_CR18","doi-asserted-by":"publisher","unstructured":"Hendrix N, Hendrix W, van Dijke K et al (2023) Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist. Eur Radiol 33:1575\u20131588. https:\/\/doi.org\/10.1007\/s00330-022-09205-4","DOI":"10.1007\/s00330-022-09205-4"},{"key":"2123_CR19","doi-asserted-by":"publisher","unstructured":"Westerhoff M, Gyftopoulos S, Dane B et al (2025) Deep learning-based opportunistic CT osteoporosis screening and the establishment of normative values. Radiology 317:e250917. https:\/\/doi.org\/10.1148\/radiol.250917","DOI":"10.1148\/radiol.250917"},{"key":"2123_CR20","doi-asserted-by":"publisher","unstructured":"Quinsten AS, Bojahr C, Nassenstein K et al (2025) Automated field of view prescription for whole-body magnetic resonance imaging using deep learning based body region segmentations. Invest Radiol https:\/\/doi.org\/10.1097\/RLI.0000000000001236","DOI":"10.1097\/RLI.0000000000001236"},{"key":"2123_CR21","doi-asserted-by":"publisher","unstructured":"Herber SK, M\u00fcller L, Pinto dos Santos D et al (2026) Diagnostic performance of artificial intelligence models for pulmonary nodule classification: a multi-model evaluation. Eur Radiol 36:537\u2013547. https:\/\/doi.org\/10.1007\/s00330-025-11845-1","DOI":"10.1007\/s00330-025-11845-1"},{"key":"2123_CR22","doi-asserted-by":"publisher","unstructured":"Hendrix W, Hendrix N, Scholten ET et al (2025) Artificial intelligence for the detection of airway nodules in chest CT scans. Eur Radiol 35:5615 \u20135625. https:\/\/doi.org\/10.1007\/s00330-025-11468-6","DOI":"10.1007\/s00330-025-11468-6"},{"key":"2123_CR23","doi-asserted-by":"publisher","unstructured":"Hendrix N, Hendrix W, Maresch B et al (2024) Artificial intelligence for automated detection and measurements of carpal instability signs on conventional radiographs. Eur Radiol 34:6600\u20136613. https:\/\/doi.org\/10.1007\/s00330-024-10744-1","DOI":"10.1007\/s00330-024-10744-1"},{"key":"2123_CR24","doi-asserted-by":"publisher","unstructured":"Jimenez-Pastor A, Lopez-Gonzalez R, Fos-Guarinos B et al (2023) Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks. Eur Radiol 33:5087\u20135096. https:\/\/doi.org\/10.1007\/s00330-023-09410-9","DOI":"10.1007\/s00330-023-09410-9"},{"key":"2123_CR25","doi-asserted-by":"publisher","unstructured":"Ono D, Dickson DW, Koga S (2024) Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: a comparative analysis with convolutional neural network model. Neuropathol Appl Neurobiol 50:e12997. https:\/\/doi.org\/10.1111\/nan.12997","DOI":"10.1111\/nan.12997"},{"key":"2123_CR26","doi-asserted-by":"publisher","unstructured":"Kong SH, Choi S, Cho W et al (2025) Enhancing vertebral fracture prediction using multitask deep learning computed tomography imaging of bone and muscle. Eur Radiol https:\/\/doi.org\/10.1007\/s00330-025-12049-3","DOI":"10.1007\/s00330-025-12049-3"},{"key":"2123_CR27","doi-asserted-by":"publisher","unstructured":"Kreutzer H, Caselitz A-S, Dratsch T et al (2025) Large language model-based uncertainty-adjusted label extraction for artificial intelligence model development in upper extremity radiography. Eur Radiol https:\/\/doi.org\/10.1007\/s00330-025-12102-1","DOI":"10.1007\/s00330-025-12102-1"},{"key":"2123_CR28","doi-asserted-by":"publisher","unstructured":"Mese I, Kocak B (2025) ChatGPT as an effective tool for quality evaluation of radiomics research. Eur Radiol 35:2030\u20132042. https:\/\/doi.org\/10.1007\/s00330-024-11122-7","DOI":"10.1007\/s00330-024-11122-7"},{"key":"2123_CR29","doi-asserted-by":"publisher","unstructured":"Ham S, Choi G, Je B-K, Oh S (2025) Development of a deep-learning model for estimating newborn gestational age via lumbar vertebral segmentation on plain radiography. Korean J Radiol 26:867\u2013876. https:\/\/doi.org\/10.3348\/kjr.2025.0172","DOI":"10.3348\/kjr.2025.0172"},{"key":"2123_CR30","doi-asserted-by":"publisher","unstructured":"Hossain MS, Rahman M, Ahmed M et al (2025) An in-depth review and analysis of deep learning methods and applications in spinal cord imaging. Healthc Anal 8:100429. https:\/\/doi.org\/10.1016\/j.health.2025.100429","DOI":"10.1016\/j.health.2025.100429"},{"key":"2123_CR31","doi-asserted-by":"publisher","unstructured":"Chen L-S, Zhu Z-Q, Li J et al (2024) Magnetic resonance imaging features for differentiating tuberculous from pyogenic spondylitis: a meta-analysis. Skeletal Radiol 53:697\u2013707. https:\/\/doi.org\/10.1007\/s00256-023-04459-5","DOI":"10.1007\/s00256-023-04459-5"},{"key":"2123_CR32","doi-asserted-by":"publisher","unstructured":"Alavi SMA, Petri F, Mahmoud OK et al (2024) Culture-negative native vertebral osteomyelitis: a narrative review of an underdescribed condition. J Clin Med 13:5802. https:\/\/doi.org\/10.3390\/jcm13195802","DOI":"10.3390\/jcm13195802"},{"key":"2123_CR33","doi-asserted-by":"crossref","unstructured":"Li Z, Zhang Q, Lian X et al (2025) Value of metagenomic next-generationsequencing in the\u00a0 diagnosisof native pyogenic spinal infections: a multicenter,retrospective observational study. Spine J 25:640 \u2013648. https:\/\/doi.org\/10.1016\/j.spinee.2024.11.004","DOI":"10.1016\/j.spinee.2024.11.004"},{"key":"2123_CR34","doi-asserted-by":"publisher","unstructured":"Chen J, Guo X, Liu X et al (2024) Differentiation of tuberculous and brucellar spondylitis using conventional MRI-based deep learning algorithms. Eur J Radiol 178:111655. https:\/\/doi.org\/10.1016\/j.ejrad.2024.111655","DOI":"10.1016\/j.ejrad.2024.111655"},{"key":"2123_CR35","doi-asserted-by":"publisher","unstructured":"Kim K, Kim S, Lee YH, Lee SH, Lee HS, Kim S (2018) Performance of the deep convolutional neural network based magnetic resonance image scoring algorithm for differentiating between tuberculous and pyogenic spondylitis. Sci Rep 8:13124. https:\/\/doi.org\/10.1038\/s41598-018-31486-3","DOI":"10.1038\/s41598-018-31486-3"},{"key":"2123_CR36","doi-asserted-by":"publisher","unstructured":"Wu S, Wei Y, Li H et al (2022) A predictive clinical-radiomics nomogram for differentiating tuberculous spondylitis from pyogenic spondylitis using CT and clinical risk factors. Infect Drug Resist 15:7327\u20137338. https:\/\/doi.org\/10.2147\/IDR.S388868","DOI":"10.2147\/IDR.S388868"},{"key":"2123_CR37","doi-asserted-by":"publisher","unstructured":"Qin C, Dai L-P, Zhang Y-L et al (2025) The value of MRI radiomics in distinguishing different types of spinal infections. Comput Methods Programs Biomed 264:108719. https:\/\/doi.org\/10.1016\/j.cmpb.2025.108719","DOI":"10.1016\/j.cmpb.2025.108719"},{"key":"2123_CR38","doi-asserted-by":"publisher","unstructured":"Nijiati M, Zhang M, Huang C et al (2025) Multimodal-imaging-based interpretable deep learning framework for distinguishing Brucella from tuberculosis spondylitis: a dual-center study. Diagnostics (Basel) 15:2963. https:\/\/doi.org\/10.3390\/diagnostics15232963","DOI":"10.3390\/diagnostics15232963"},{"key":"2123_CR39","doi-asserted-by":"publisher","unstructured":"Cammarota S, Ferrante M, Carosi A, D\u2019Angelillo RM, Toschi N (2026) Beam angle optimization for radiotherapy using LLMs via reinforcement-learning inspired iterative refinement. Med Phys 53:e70258. https:\/\/doi.org\/10.1002\/mp.70258","DOI":"10.1002\/mp.70258"},{"key":"2123_CR40","doi-asserted-by":"publisher","unstructured":"Liu NF, Lin K, Hewitt J et al (2024) Lost in the middle: how language models use long contexts. Trans Assoc Comput Linguist 12:157\u2013173. https:\/\/doi.org\/10.1162\/tacl_a_00638","DOI":"10.1162\/tacl_a_00638"},{"key":"2123_CR41","doi-asserted-by":"publisher","unstructured":"Nowak S, Wulff B, Layer YC et al (2025) Privacy-ensuring open-weights large language models are competitive with closed-weights GPT-4o in extracting chest radiography findings from free-text reports. Radiology 314:e240895. https:\/\/doi.org\/10.1148\/radiol.240895","DOI":"10.1148\/radiol.240895"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02123-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02123-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02123-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T15:41:30Z","timestamp":1784562090000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02123-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,20]]},"references-count":41,"alternative-id":["2123"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02123-7","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,20]]},"assertion":[{"value":"28 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This retrospective two-center diagnostic study was conducted in accordance with the Declaration of Helsinki and its later amendments. The study was approved by the Ethics Committee of University of South China Affiliated Nanhua Hospital (approval no. 2025-KY-036; approval date: June 12, 2025) and the Ethics Committee of Hunan University of Medicine General Hospital (approval no. KY-2025061802; approval date: June 18, 2025).","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The requirement for written informed consent was waived by the ethics committees because of the retrospective nature of the study and the use of anonymized imaging and clinical data.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}