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Deep learning-based lightweight model for automated lumbar foraminal stenosis classification: sagittal CT diagnostic performance compared to clinical subspecialists.

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Bibliographic Details
Title: Deep learning-based lightweight model for automated lumbar foraminal stenosis classification: sagittal CT diagnostic performance compared to clinical subspecialists.
Authors: Huang JW; The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China., Zhang YL; The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China., Li KY; The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China., Li HL; The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China., Ye HB; The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China., Chen YH; The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China., Lin XX; The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China., Tian NF; The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China. tiannaifeng@163.com.
Source: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society [Eur Spine J] 2026 Feb; Vol. 35 (2), pp. 583-590. Date of Electronic Publication: 2025 Aug 23.
Publication Type: Journal Article; Comparative Study
Journal Info: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 9301980 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-0932 (Electronic) Linking ISSN: 09406719 NLM ISO Abbreviation: Eur Spine J Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1432-0932
DOI:10.1007/s00586-025-09281-2