Evidence map›Paper›PMID 41656429›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Using artificial intelligence and a walking aid to improve lumbar stability parameter evaluation methods.

Fanguo Lin, Yuye Zhang, Yingzi Zhang, Wenxiang Tang, Yanping Niu, Jun Hua, Yongming Sun, Xiaozhong Zhou

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Article in 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, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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8 authors.

Fanguo LinDepartment of Orthopedics, The Second Affiliated Hospital of Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, China.
Yuye ZhangDepartment of Orthopedics, The Second Affiliated Hospital of Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, China.
Yingzi ZhangDepartment of Orthopedics, The Second Affiliated Hospital of Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, China.
Wenxiang TangDepartment of Orthopedics, The Second Affiliated Hospital of Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, China.
Yanping NiuDepartment of Orthopedics, The Second Affiliated Hospital of Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, China.
Jun HuaDepartment of Orthopedics, The Second Affiliated Hospital of Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, China.
Yongming SunDepartment of Orthopedics, The Second Affiliated Hospital of Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, China.
Xiaozhong ZhouDepartment of Orthopedics, The Second Affiliated Hospital of Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, China. xiaozhongzhou01@163.com.ORCID 0009-0004-5062-4556

Funding

Science and Education Strong Health Project of Suzhou MSXM2024011Science and Technology Program of Suzhou SKYD2022094; SKYD2023085
6 · The paper itself

Abstract

purposeTo refine a method of assessing lumbar stability by applying artificial intelligence and incorporating the use of a walking aid during imaging procedures.

methodsA software application was developed to evaluate lumbar stability parameters using machine learning and neural network models (Swin-Resnet). The intra-class correlation coefficient (ICC) was used to assess the agreement between Swin-Resnet and the evaluations conducted by three spine surgeons, each with over ten years of experience. Subsequently, traditional flexion-extension radiographs (FET) and flexion-extension radiographs with a walking aid (FEW) were performed on the enrolled patients with lumbar spondylolisthesis. The developed software was used to measure parameters such as sagittal translation, segmental angulation, and detection rate of lumbar instability.

resultsThere was no significant statistical difference in the average error between the Swin-Resnet model and physicians in terms of sagittal translation, segmental angulation, and agreement of lumbar instability assessment. The average values of sagittal translation and segmental angulation were significantly higher in FEW than in FET (2.40 (1.09, 3.59) vs. 0.56 (0.16, 1.40); 8.00 (5.00, 10.25) vs. 2.00 (1.00, 5.25), P < 0.05). Among 50 patients, FEW detected 19 cases (38%) of lumbar instability, while FET detected only 3 cases (6%). Additionally, five cases (10%) exhibited a posterior opening angle ≥ 5° in FEW, whereas none met this threshold in FET.

conclusionThe incorporation of a walking aid in lumbar flexion-extension radiographs and the development of automated parameter measurement software facilitates a more accurate and consistent evaluation of lumbar stability in patients with lumbar spondylolisthesis.

Indexed as

Artificial IntelligenceJoint InstabilityLumbar VertebraeSpondylolisthesisFemaleHumansMachine LearningMaleMiddle AgedNeural Networks, ComputerRadiographyWalkingArtificial intelligenceFlexion-extension radiographsLumbar stabilityWalking aid

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.