Journal of Shandong University (Health Sciences) ›› 2026, Vol. 64 ›› Issue (2): 1-10.doi: 10.6040/j.issn.1671-7554.0.2025.0219

• Advances in Intelligent Orthopaedics-Expert Consensus •    

Expert consensus on measurement sites and annotation of artificial intelligence-based spinal degenerative imaging(2025)

Intelligent Orthopedics Subgroup of Chinese Association of Orthopedic, Subgroup for Prevention and Control of Spinal and Spinal Cord Injury Diseases of Professional Committee for Prevention and Control of Spinal Diseases of Chinese Preventive Medicine Association   

  1. Intelligent Orthopedics Subgroup of Chinese Association of Orthopedic, Subgroup for Prevention and Control of Spinal and Spinal Cord Injury Diseases of Professional Committee for Prevention and Control of Spinal Diseases of Chinese Preventive Medicine Association
  • Published:2026-02-10

Abstract: Imaging examination is one of the main methods to assess the degree of spinal degeneration, but the standardization of disease diagnosis and treatment is limited due to differences in imaging equipment and imaging scanning modalities in different medical institutions, complicated measurement data, and controversial description and grading of imaging results. The application of artificial intelligence(AI)to spinal degenerative disease imaging analysis can improve the consistency of disease diagnostic criteria, as well as enhance the diagnostic efficiency of physicians, benefiting both doctors and patients. In order to standardize the annotation and measurement of spinal degenerative disease image and to promote the better application of AI in clinical practice, with reference to the latest domestic and international literature, clinical research data and relevant industry requirements, experts have formulated a unified opinion on spinal degenerative disease imaging annotation data acquisition specifications, definitions, imaging performances, and measurement protocols and formulated this consensus, which can help improve the consistency of data annotation and measurement, and then establish an AI algorithm model with high accuracy, good versatility and strong generalization ability, providing a solid imaging basis for the standardized diagnosis and treatment of spinal degenerative diseases.

Key words: Spinal degenerative diseases, Imaging annotation, Artificial intelligence, Diagnosis, Expert consensus

CLC Number: 

  • R681.5
[1] Endean A, Palmer KT, Coggon D. Potential of magnetic resonance imaging findings to refine case definition for mechanical low back pain in epidemiological studies: a systematic review[J]. Spine(Phila Pa 1976), 2011, 36(2): 160-169.
[2] Bono CM, Ghiselli G, Gilbert TJ, et al. An evidence-based clinical guideline for the diagnosis and treatment of cervical radiculopathy from degenerative disorders[J]. Spine J, 2011, 11(1): 64-72.
[3] Jiang Y, Yang M, Wang S, et al. Emerging role of deep learning-based artificial intelligence in tumor pathology[J]. Cancer Commun(Lond), 2020, 40(4): 154-166.
[4] 刘士远, 陈敏. 医学影像学导论[M]. 北京: 人民卫生出版社, 2022.
[5] 黄霖, 车圳, 李明, 等. 人工智能在骨科疾病诊治中的研究进展[J]. 山东大学学报(医学版), 2023, 61(3): 37-45. HUANG Lin, CHE Zhen, LI Ming, et al. Research advances of artificial intelligence in the diagnosis and treatment of orthopaedic diseases[J]. Journal of Shandong University(Health Sciences), 2023, 61(3): 37-45.
[6] 刘琚, 吴强, 于璐跃, 等. 基于深度学习的脑肿瘤图像分割[J]. 山东大学学报(医学版), 2020, 58(8): 42-49. LIU Ju, WU Qiang, YU Luyue, et al. Brain tumor image segmentation based on deep learning techniques[J]. Journal of Shandong University(Health Sciences), 2020, 58(8): 42-49.
[7] Balkenende L, Teuwen J, Mann RM. Application of deep learning in breast cancer imaging[J]. Semin Nucl Med, 2022, 52(5): 584-596.
[8] Hayashi D. Deep learning for lumbar spine MRI reporting: a welcome tool for radiologists[J]. Radiology, 2021, 300(1): 139-140.
[9] Lim DSW, Makmur A, Zhu L, et al. Improved productivity using deep learning-assisted reporting for lumbar spine MRI[J]. Radiology, 2022, 305(1): 160-166.
[10] Hallinan JTPD, Zhu L, Yang K, et al. Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI[J]. Radiology, 2021, 300(1): 130-138.
[11] 中华医学会放射学分会医学影像大数据与人工智能工作委员会,中国研究型医院学会医学影像与人工智能专业委员会,中国医师协会临床精准医疗专业委员会,等. 影像组学多模态MRI量化分析用于直肠癌新辅助治疗后疗效及预后评估的方法学专家共识[J]. 中华放射学杂志, 2022, 56(6): 608-615.
[12] 冯世庆. 计算机视觉与腰椎退行性疾病[J]. 山东大学学报(医学版), 2023, 61(3): 1-6. FENG Shiqing. Computer vision and lumbar degenerative disease[J]. Journal of Shandong University(Health Sciences), 2023, 61(3): 1-6.
[13] Alukaev D, Kiselev S, Mustafaev T, et al. A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation[J]. Eur Spine J, 2022, 31(8): 2115-2124.
[14] 中国医师协会放射学分会“互联网+”影像学组. “互联网+”医学影像诊断中国专家共识(2019版)[J]. 中华医学杂志, 2019, 99(43): 3398-3402.
[15] Suri A, Jones BC, Ng G, et al. Vertebral deformity measurements at MRI, CT, and radiography using deep learning[J]. Radiol Artif Intell, 2022, 4(1): e210015. doi: 10.1148/ryai.2021210015
[16] 国家药品监督管理局. 人工智能医疗器械 质量要求和评价 第2部分: 数据集通用要求: YY/T 1833.2—2022[S]. 北京:中国标准出版社, 2022: 7.
[17] Hu H, Wang X, Yang H, et al. Development and validation of an automatic diagnostic tool for lumbar stability based on deep learning[J]. Chin J Repar Reconstr Surg, 2023, 37(1): 81-90.
[18] Pang SM, Su ZH, Leung S, et al. Direct automated quantitative measurement of spine by cascade amplifier regression network with manifold regularization[J]. Med Image Anal, 2019, 55: 103-115. doi: 10.1016/j.media.2019.04.012
[19] Fujimori T, Suzuki Y, Takenaka S, et al. Development of artificial intelligence for automated measurement of cervical lordosis on lateral radiographs[J]. Sci Rep, 2022, 12(1): 15732. doi: 10.1038/s41598-022-19914-x
[20] Cho BH, Kaji D, Cheung ZB, et al. Automated mea-surement of lumbar lordosis on radiographs using machine learning and computer vision[J]. Global Spine J, 2020, 10(5): 611-618.
[21] Roberts MG, Oh T, Pacheco EMB, et al. Semi-automatic determination of detailed vertebral shape from lumbar radiographs using active appearance models[J]. Osteoporos Int, 2012, 23(2): 655-664.
[22] Ha AY, Do BH, Bartret AL, et al. Automating scoliosis measurements in radiographic studies with machine lear-ning: comparing artificial intelligence and clinical reports[J]. J Digit Imag, 2022, 35(3): 524-533.
[23] Forsberg D, Sj blom E, Sunshine JL. Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data[J]. J Digit Imag, 2017, 30(4): 406-412.
[24] Dützmann S, Fernandez R, Rosenthal D. Thoracic spinal stenosis: etiology, pathogenesis, and treatment[J]. Orthopade, 2019, 48(10): 844-848.
[25] Melancia JL, Francisco AF, Antunes JL. Spinal stenosis[J]. Handb Clin Neurol, 2014, 119: 541-549. doi: 10.1016/B978-0-7020-4086-3.00035-7
[26] Gupta SK, Roy RC, Srivastava A. Sagittal diameter of the cervical canal in normal Indian adults[J]. Clin Radiol, 1982, 33(6): 681-685.
[27] Matsunaga S, Sakou T. Ossification of the posterior longitudinal ligament of the cervical spine: etiology and na-tural history[J]. Spine(Phila Pa 1976),2012,37(5):309-314.
[28] Chau AMT, Pelzer NR, Hampton J, et al. Lateral extent and ventral laminar attachments of the lumbar ligamentum flavum: cadaveric study[J]. Spine J, 2014, 14(10): 2467-2471.
[29] Zhang B, Chen G, Chen X, et al. Cervical ossification of ligamentum flavum: elaborating an underappreciated but occasional contributor to myeloradiculopathy in aging population based on synthesis of individual participant data[J]. Clin Interv Aging, 2021, 16: 897-908. doi: 10.2147/CIA.S313357
[30] Epstein NE, Epstein JA, Carras R, et al. Coexisting cervical and lumbar spinal stenosis: diagnosis and management[J]. Neurosurgery, 1984, 15(4): 489-496.
[31] 党耕町, 王超, 陈仲强, 等. 颈椎侧位X线片椎管与椎体矢状径比值的测量及统计分析 [J]. 中华骨科杂志,1993,13(4): 264-267.
[32] Seo J, Lee JW. 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[J]. Korean J Radiol, 2023, 24(3): 224-234.
[33] Baron EM, Young WF. Cervical spondylotic myelopathy: a brief review of its pathophysiology, clinical course, and diagnosis[J]. Neurosurgery, 2007, 60(Supp1 1): 35-41.
[34] Bazzocchi A, Guglielmi G. Spine imaging[J]. Semin Musculoskelet Radiol, 2022, 26(4): 385-386.
[35] Medow JE, Trost G, Sandin J. Surgical management of cervical myelopathy: indications and techniques for surgical corpectomy[J]. Spine J, 2006, 6(6): 233-241.
[36] Cowley P. Neuroimaging of spinal canal stenosis[J]. Magn Reson Imaging Clin N Am, 2016, 24(3): 523-539.
[37] Byvaltsev VA, Kalinin AA, Hernandez PA, et al. Mole-cular and genetic mechanisms of spinal stenosis formation: systematic review[J]. Int J Mol Sci, 2022, 23(21): 13479.
[38] Di Stasio GD, Mansi L. Simone waldt and klaus woertler(eds): measurements and classifications in musculoske-letal radiology[J]. Eur J Nucl Med Mol Imag, 2014, 41(10): 1991.
[39] 马赛,何达,田伟.胸椎管狭窄症的诊断与治疗进展[J]. 骨科临床与研究杂志, 2018, 3(5): 276-281.
[40] Aizawa T, Sato T, Sasaki H, et al. Thoracic myelopathy caused by ossification of the ligamentum flavum: clinical features and surgical results in the Japanese population[J]. J Neurosurg Spine, 2006, 5(6): 514-519.
[41] Sanghvi AV, Chhabra HS, Mascarenhas AA, et al. Thoracic myelopathy due to ossification of ligamentum flavum: a retrospective analysis of predictors of surgical outcome and factors affecting preoperative neurological status[J]. Eur Spine J, 2011, 20(2): 205-215.
[42] 中国康复医学会骨质疏松预防与康复专业委员会, 中国老年保健协会骨科微创分会. 退行性腰椎管狭窄症诊疗专家共识[J]. 中华骨与关节外科杂志, 2023, 16(2): 97-103.
[43] Botwin KP, Gruber RD. Lumbar spinal stenosis: anatomy and pathogenesis[J]. Phys Med Rehabil Clin N Am, 2003, 14(1): 1-15.
[44] 中华人民共和国司法部. 法医临床影像学检验实施规范: SF/T 0112—2021[S]. 北京:中国标准出版社, 2021: 11.
[45] Pfirrmann CW, Metzdorf A, Zanetti M, et al. Magnetic resonance classification of lumbar intervertebral disc degeneration[J]. Spine(Phila Pa 1976), 2001, 26(17): 1873-1878.
[46] Ren X, Liu H, Hui S, et al. Forecast of pain degree of lumbar disc herniation based on back propagation neural network[J]. Open Life Sci, 2023, 18(1): 20220673. doi: 10.1515/biol-2022-0673
[47] Kreiner DS, Shaffer WO, Baisden JL, et al. An evidence-based clinical guideline for the diagnosis and treatment of degenerative lumbar spinal stenosis(update)[J]. Spine J, 2013, 13(7): 734-743.
[48] Wassenaar M, van Rijn RM, van Tulder MW, et al. Magnetic resonance imaging for diagnosing lumbar spinal pathology in adult patients with low back pain or sciatica: a diagnostic systematic review[J]. Eur Spine J, 2012, 21(2): 220-227.
[49] Weishaupt D, Zanetti M, Boos N, et al. MR imaging and CT in osteoarthritis of the lumbar facet joints[J]. Skeletal Radiol, 1999, 28(4): 215-219.
[50] Battaglia PJ, Maeda Y, Welk A, et al. Reliability of the goutallier classification in quantifying muscle fatty degeneration in the lumbar multifidus using magnetic resonance imaging[J]. J Manip Physiol Ther, 2014, 37(3): 190-197.
[51] Goutallier D, Postel JM, Bernageau J, et al. Fatty muscle degeneration in cuff ruptures. Pre- and postoperative evaluation by CT scan[J]. Clin Orthop Relat Res, 1994(304): 78-83.
[52] Mikhael MA, Ciric I, Tarkington JA, et al. Neuroradiological evaluation of lateral recess syndrome[J]. Radiology, 1981, 140(1): 97-107.
[53] Varol T, Iyem C, Cezayirli E, et al. Comparative morphometry of the lower lumbar vertebrae: osteometry in dry bones and computed tomography images of patients with and without low back pain[J]. J Int Med Res, 2006, 34(3): 316-330.
[54] Wu AM, Zou F, Cao Y, et al. Lumbar spinal stenosis: an update on the epidemiology, diagnosis and treatment[J]. AME Medical Journal, 2017, 2(5): 63. doi:10.21037/amj.2017.04.13
[55] Englund J. Lumbar spinal stenosis[J]. Curr Sports Med Rep, 2007, 6(1): 50-55.
[56] Alsaleh K, Ho D, Rosas-Arellano MP, et al. Radiographic assessment of degenerative lumbar spinal stenosis: is MRI superior to CT? [J]. Eur Spine J, 2017, 26(2): 362-367.
[57] 汪洋,查云飞,邢栋. 腰椎旁肌肉脂肪含量与椎间盘退变关系的定量MRI研究[J]. 磁共振成像, 2018, 9(11): 819-824. WANG Yang, ZHA Yunfei, XING Dong. Quantitative MRI study of the relationship between fat content in lumbar paravertebral muscles and disc degeneration[J]. Chinese Journal of Magnetic Resonance Imaging, 2018, 9(11): 819-824.
[58] Balkissoon AR. Radiologic interpretation of vacuum phenomena[J]. Crit Rev Diagn Imaging, 1996, 37(5): 435-460.
[59] Zhang C, Lin Y, Han Z, et al. Feasibility of T2 mapping and magnetic transfer ratio for diagnosis of intervertebral disc degeneration at the cervicothoracic junction: a pilot study[J]. Biomed Res Int, 2019, 2019: 6396073. doi: 10.1155/2019/6396073
[60] Lu K, Gao X, Tong T, et al. Clinical predictors of surgical outcomes and imaging features in single segmental cervical spondylotic myelopathy with lower cervical instability[J]. Med Sci Monit, 2017, 23: 3697-3705. doi: 10.12659/msm.906046
[61] White AA, Johnson RM, Panjabi MM, et al. Biomechanical analysis of clinical stability in the cervical spine[J]. Clin Orthop Relat Res, 1975(109): 85-96.
[62] Wang Y, Huang K. Research progress of diagnosing metho-dology for lumbar segmental instability: a narrative review[J]. Medicine(Baltimore), 2022,101(1):e28534. doi: 10.1097/MD.0000000000028534
[63] Burke CJ, Shah D, Saha S, et al. Spondylolisthesis: a pictorial review[J]. Br J Hosp Med(Lond), 2012, 73(12): 691-695.
[64] McAviney J, Schulz D, Bock R, et al. Determining the relationship between cervical lordosis and neck complaints[J]. J Manipulative Physiol Ther, 2005, 28(3): 187-193.
[65] Harrison DE, Harrison DD, Cailliet R, et al. Cobb method or Harrison posterior tangent method: which to choose for lateral cervical radiographic analysis[J]. Spine(Phila Pa 1976), 2000, 25(16): 2072-2078.
[66] Kado DM, Prenovost K, Crandall C. Narrative review: hyperkyphosis in older persons[J]. Ann Intern Med, 2007, 147(5): 330-338.
[67] Been E, Kalichman L. Lumbar lordosis[J]. Spine J, 2014, 14(1): 87-97.
[68] 刘全金, 嵇文, 胡浪涛, 等. 基于双解码器的医学图像分割模型[J]. 山东大学学报(工学版), 2024, 54(6): 8-18. LIU Quanjin, JI Wen, HU Langtao, et al. Medical image segmentation model based on double decoder[J]. Journal of Shandong University(Engineering Science), 2024, 54(6): 8-18.
[69] 赵杰, 张凯, 陈辰. 腰椎矢状位力线分型及临床意义[J]. 山东大学学报(医学版), 2019, 57(5): 13-17. ZHAO Jie, ZHANG Kai, CHEN Chen. Classification of the sagittal alignment of lumbar spine and its clinical significance[J]. Journal of Shandong University(Health Sciences), 2019, 57(5): 13-17.
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