{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:25:36Z","timestamp":1786980336296,"version":"build-2736575974"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:00:00Z","timestamp":1755907200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:00:00Z","timestamp":1755907200000},"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":["Eur Spine J"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s00586-025-09281-2","type":"journal-article","created":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T07:00:17Z","timestamp":1755932417000},"page":"583-590","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning-based lightweight model for automated lumbar foraminal stenosis classification: sagittal CT diagnostic performance compared to clinical subspecialists"],"prefix":"10.1007","volume":"35","author":[{"given":"Jia-Wei","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Long","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai-Yu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua-Lin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao-Bo","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Han","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin-Xiao","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nai-Feng","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,23]]},"reference":[{"issue":"3","key":"9281_CR1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.3344\/kjp.2019.32.3.147","volume":"32","author":"YK Choi","year":"2019","unstructured":"Choi YK (2019) Lumbar foraminal neuropathy: an update on non-surgical management. Korean J Pain 32(3):147\u2013159. https:\/\/doi.org\/10.3344\/kjp.2019.32.3.147","journal-title":"Korean J Pain"},{"issue":"7","key":"9281_CR2","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1016\/j.spinee.2009.03.005","volume":"9","author":"L Kalichman","year":"2009","unstructured":"Kalichman L, Cole R, Kim DH, Li L, Suri P, Guermazi A et al (2009) Spinal stenosis prevalence and association with symptoms: the Framingham study. Spine J 9(7):545\u2013550. https:\/\/doi.org\/10.1016\/j.spinee.2009.03.005","journal-title":"Spine J"},{"issue":"4","key":"9281_CR3","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1007\/s40122-024-00607-4","volume":"13","author":"VM Silva-Ortiz","year":"2024","unstructured":"Silva-Ortiz VM, Abd-Elsayed A, Medina-Razcon J, Robinson CL (2024) Percutaneous foraminal neuroplasty using reference spinal needles: technical description. Pain Ther 13(4):1023\u20131029. https:\/\/doi.org\/10.1007\/s40122-024-00607-4","journal-title":"Pain Ther"},{"issue":"1","key":"9281_CR4","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1148\/radiol.2021204289","volume":"300","author":"J Hallinan","year":"2021","unstructured":"Hallinan J, Zhu L, Yang K, Makmur A, Algazwi DAR, Thian YL et al (2021) Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI. Radiology 300(1):130\u2013138. https:\/\/doi.org\/10.1148\/radiol.2021204289","journal-title":"Radiology"},{"issue":"1","key":"9281_CR5","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s12891-021-04949-4","volume":"23","author":"H Banitalebi","year":"2022","unstructured":"Banitalebi H, Espeland A, Anvar M, Hermansen E, Hellum C, Brox JI et al (2022) Reliability of preoperative MRI findings in patients with lumbar spinal stenosis. BMC Musculoskelet Disord 23(1):51. https:\/\/doi.org\/10.1186\/s12891-021-04949-4","journal-title":"BMC Musculoskelet Disord"},{"key":"9281_CR6","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1097\/00003086-200103000-00015","volume":"384","author":"LA Saint-Louis","year":"2001","unstructured":"Saint-Louis LA (2001) Lumbar spinal stenosis assessment with computed tomography, magnetic resonance imaging, and myelography. Clin Orthop Relat Res 384:122\u2013136. https:\/\/doi.org\/10.1097\/00003086-200103000-00015","journal-title":"Clin Orthop Relat Res"},{"issue":"10","key":"9281_CR7","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.1007\/s40618-022-01837-z","volume":"45","author":"E Biamonte","year":"2022","unstructured":"Biamonte E, Levi R, Carrone F, Vena W, Brunetti A, Battaglia M et al (2022) Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures. J Endocrinol Invest 45(10):2007\u20132017. https:\/\/doi.org\/10.1007\/s40618-022-01837-z","journal-title":"J Endocrinol Invest"},{"issue":"1","key":"9281_CR8","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s13244-022-01227-2","volume":"13","author":"H Liu","year":"2022","unstructured":"Liu H, Jiao M, Yuan Y, Ouyang H, Liu J, Li Y et al (2022) Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI. Insights Imaging 13(1):87. https:\/\/doi.org\/10.1186\/s13244-022-01227-2","journal-title":"Insights Imaging"},{"issue":"6","key":"9281_CR9","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.21037\/qims-21-939","volume":"12","author":"B Qu","year":"2022","unstructured":"Qu B, Cao J, Qian C, Wu J, Lin J, Wang L et al (2022) Current development and prospects of deep learning in spine image analysis: a literature review. Quant Imaging Med Surg 12(6):3454\u20133479. https:\/\/doi.org\/10.21037\/qims-21-939","journal-title":"Quant Imaging Med Surg"},{"issue":"1","key":"9281_CR10","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230614","volume":"310","author":"Y Kim","year":"2024","unstructured":"Kim Y, Kim YG, Park JW, Kim BW, Shin Y, Kong SH et al (2024) A CT-based deep learning model for predicting subsequent fracture risk in patients with hip fracture. Radiology 310(1):e230614. https:\/\/doi.org\/10.1148\/radiol.230614","journal-title":"Radiology"},{"issue":"2","key":"9281_CR11","doi-asserted-by":"publisher","first-page":"e1327","DOI":"10.1002\/jsp2.1327","volume":"7","author":"B Zhang","year":"2024","unstructured":"Zhang B, Chen K, Yuan H, Liao Z, Zhou T, Guo W et al (2024) Automatic lenke classification of adolescent idiopathic scoliosis with deep learning. JOR Spine 7(2):e1327. https:\/\/doi.org\/10.1002\/jsp2.1327","journal-title":"JOR Spine"},{"issue":"3\u20134","key":"9281_CR12","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s12021-018-9365-1","volume":"16","author":"Z Han","year":"2018","unstructured":"Han Z, Wei B, Leung S, Nachum IB, Laidley D, Li S (2018) Automated pathogenesis-based diagnosis of lumbar neural foraminal stenosis via deep multiscale multitask learning. Neuroinformatics 16(3\u20134):325\u2013337. https:\/\/doi.org\/10.1007\/s12021-018-9365-1","journal-title":"Neuroinformatics"},{"issue":"4","key":"9281_CR13","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.2214\/AJR.09.2772","volume":"194","author":"S Lee","year":"2010","unstructured":"Lee S, Lee JW, Yeom JS, Kim KJ, Kim HJ, Chung SK et al (2010) A practical MRI grading system for lumbar foraminal stenosis. AJR Am J Roentgenol 194(4):1095\u20131098. https:\/\/doi.org\/10.2214\/AJR.09.2772","journal-title":"AJR Am J Roentgenol"},{"key":"9281_CR14","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/1475-9276-12-39","volume":"12","author":"D He","year":"2013","unstructured":"He D, Yu H, Chen Y (2013) Equity in the distribution of CT and MRI in china: a panel analysis. Int J Equity Health 12:39. https:\/\/doi.org\/10.1186\/1475-9276-12-39","journal-title":"Int J Equity Health"},{"key":"9281_CR15","doi-asserted-by":"publisher","unstructured":"Charouh Z, Ezzouhri A, Ghogho M, Guennoun ZA, Resource-Efficient (2022) CNN-Based method for moving vehicle detection. Sens (Basel) 22(3). https:\/\/doi.org\/10.3390\/s22031193","DOI":"10.3390\/s22031193"},{"issue":"12","key":"9281_CR16","doi-asserted-by":"publisher","first-page":"7568","DOI":"10.1002\/mp.16722","volume":"50","author":"G Kumar","year":"2023","unstructured":"Kumar G, Sharma N, Paul A (2023) An extremely lightweight CNN model for the diagnosis of chest radiographs in resource-constrained environments. Med Phys 50(12):7568\u20137578. https:\/\/doi.org\/10.1002\/mp.16722","journal-title":"Med Phys"},{"key":"9281_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2023.118802","volume":"345","author":"J Wang","year":"2023","unstructured":"Wang J, Dong J, Tang M, Yao J, Li X, Kong D et al (2023) Identification and detection of microplastic particles in marine environment by using improved faster R-CNN model. J Environ Manage 345:118802. https:\/\/doi.org\/10.1016\/j.jenvman.2023.118802","journal-title":"J Environ Manage"},{"key":"9281_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/s22041660","author":"BM Mathunjwa","year":"2022","unstructured":"Mathunjwa BM, Lin YT, Lin CH, Abbod MF, Sadrawi M, Shieh JS (2022) ECG recurrence plot-based arrhythmia classification using two-dimensional deep residual CNN features. Sensors (Basel). https:\/\/doi.org\/10.3390\/s22041660","journal-title":"Sensors (Basel)"},{"key":"9281_CR19","doi-asserted-by":"publisher","first-page":"100392","DOI":"10.1016\/j.measen.2022.100392","volume":"23","author":"A Arivoli","year":"2022","unstructured":"Arivoli A, Golwala D, Reddy R, CoviExpert (2022) COVID-19 detection from chest X-ray using CNN. Measur Sens 23:100392. https:\/\/doi.org\/10.1016\/j.measen.2022.100392","journal-title":"Measur Sens"},{"key":"9281_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/biom10111534","author":"N Yamamoto","year":"2020","unstructured":"Yamamoto N, Sukegawa S, Kitamura A, Goto R, Noda T, Nakano K et al (2020) Deep learning for osteoporosis classification using hip radiographs and patient clinical covariates. Biomolecules. https:\/\/doi.org\/10.3390\/biom10111534","journal-title":"Biomolecules"},{"key":"9281_CR21","doi-asserted-by":"publisher","DOI":"10.1097\/BRS.0000000000005156","author":"KY Li","year":"2024","unstructured":"Li KY, Ye HB, Zhang YL, Huang JW, Li HL, Tian NF (2024) Enhancing diagnostic accuracy of fresh vertebral compression fractures with deep learning models. Spine (Phila Pa 1976). https:\/\/doi.org\/10.1097\/BRS.0000000000005156","journal-title":"Spine (Phila Pa 1976)"},{"key":"9281_CR22","doi-asserted-by":"publisher","unstructured":"Lu J-T, Pedemonte S, Bizzo B, Doyle S, Andriole KP, Michalski MH et al (2018) Deep Spine: Automated Lumbar Vertebral Segmentation, Disc-Level Designation, and Spinal Stenosis Grading using Deep Learning. In: Finale D-V, Jim F, Ken J, David K, Rajesh R, Byron W, editors. Proceedings of the 3rd Machine Learning for Healthcare Conference; Proceedings of Machine Learning Research: PMLR; pp. 403\u201319. https:\/\/doi.org\/10.48550\/arXiv.1807.10215","DOI":"10.48550\/arXiv.1807.10215"},{"issue":"3","key":"9281_CR23","doi-asserted-by":"publisher","first-page":"224","DOI":"10.3348\/kjr.2022.0351","volume":"24","author":"J Seo","year":"2023","unstructured":"Seo J, Lee JW (2023) Magnetic resonance imaging grading systems for central canal and neural foraminal stenoses of the lumbar and cervical spines with a focus on the Lee grading system. Korean J Radiol 24(3):224\u2013234. https:\/\/doi.org\/10.3348\/kjr.2022.0351","journal-title":"Korean J Radiol"}],"container-title":["European Spine Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00586-025-09281-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00586-025-09281-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00586-025-09281-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T01:33:50Z","timestamp":1772069630000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00586-025-09281-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,23]]},"references-count":23,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["9281"],"URL":"https:\/\/doi.org\/10.1007\/s00586-025-09281-2","relation":{},"ISSN":["0940-6719","1432-0932"],"issn-type":[{"value":"0940-6719","type":"print"},{"value":"1432-0932","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,23]]},"assertion":[{"value":"22 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}