ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2016
Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images.
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, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04239638 (Analysis of Cervical Spinal MRI With Deep Learning), which is not on this map. Cited by 21 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Analysis of Cervical Spinal MRI With Deep Learning
Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis.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 · 2023Pooled it
- Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Automated detection of spinal bone marrow oedema in axial spondyloarthritis: training and validation using two large phase 3 trial datasets.Rheumatology (Oxford, England) · 2025Trial
- An Automatized Deep Segmentation and Classification Model for Lumbar Disk Degeneration and Clarification of Its Impact on Clinical Decisions.Global spine journal · 2025Article
- External validation of SpineNetV2 on a comprehensive set of radiological features for grading lumbosacral disc pathologies.North American Spine Society journal · 2024Article
- Neural network segmentation of disc volume from magnetic resonance images and the effect of degeneration and spinal level.JOR spine · 2024Article
- Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images.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 · 2023Article
- The association of lumbar disc degeneration with lumbar vertebral trabecular volumetric bone mineral density in an urban population of young and middle-aged community-dwelling Chinese adults: a cross-sectional study.Journal of bone and mineral metabolism · 2023Article
- The lumbar spinal endplate lesions grades and association with lumbar disc disorders, and lumbar bone mineral density in a middle-young general Chinese population.BMC musculoskeletal disorders · 2023Observational
- External validation of the deep learning system "SpineNet" for grading radiological features of degeneration on MRIs of the lumbar spine.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 · 2022Article
- Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.Nature communications · 2022Article
- SPIE Computer-Aided Diagnosis conference anniversary review.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Key LncRNAs Associated With Oxidative Stress Were Identified by GEO Database Data and Whole Blood Analysis of Intervertebral Disc Degeneration Patients.Frontiers in genetics · 2022Article
- Differentially-expressed mRNAs, microRNAs and long noncoding RNAs in intervertebral disc degeneration identified by RNA-sequencing.Bioengineered · 2021Article
- Repair of symptomatic bilateral L5 spondylolysis with autogenous iliac crest graft and temporary intersegmental pedicle screw fixation in youth.Journal of orthopaedic surgery and research · 2021Article
- The Role of Unfolded Protein Response in Human Intervertebral Disc Degeneration: Perk and IRE1-Oxidative medicine and cellular longevity · 2021Article
- Lumbar intervertebral disc characterization through quantitative MRI analysis: An automatic voxel-based relaxometry approach.Magnetic resonance in medicine · 2020Article
- Luteoloside Inhibits IL-1β-Induced Apoptosis and Catabolism in Nucleus Pulposus Cells and Ameliorates Intervertebral Disk Degeneration.Frontiers in pharmacology · 2019Article
- Ghrelin protects against nucleus pulposus degeneration through inhibition of NF-κB signaling pathway and activation of Akt signaling pathway.Oncotarget · 2017Article
- ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist.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 · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe primary goal of this article is to achieve an automatic and objective method to compute the Pfirrmann's degeneration grade of intervertebral discs (IVD) from MRI. This grading system is used in the diagnosis and management of patients with low back pain (LBP). In addition, biomechanical models, which are employed to assess the treatment on patients with LBP, require this grading value to compute proper material properties. MATERIALS AND
methodsT2-weighted MR images of 48 patients were employed in this work. The 240 lumbar IVDs were divided into a training set (140) and a testing set (100). Three experts manually classified the whole set of IVDs using the Pfirrmann's grading system and the ground truth was selected as the most voted value among them. The developed method employs active contour models to delineate the boundaries of the IVD. Subsequently, the classification is achieved using a trained Neural Network (NN) with eight designed features that contain shape and intensity information of the IVDs.
resultsThe classification method was evaluated using the testing set, resulting in a mean specificity (95.5 %) and sensitivity (87.3 %) comparable to those of every expert with respect to the ground truth.
conclusionsOur results show that the automatic method and humans perform equally well in terms of the classification accuracy. However, human annotations have inherent inter- and intra-observer variabilities, which lead to inconsistent assessments. In contrast, the proposed automatic method is objective, being only dependent on the input MRI.
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Registered trials
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.