ArticleDiagnostics (Basel, Switzerland)2021
Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 53 citations in OpenAlex.
- Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled 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 Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- External validation of SpineNetv2 deep learning system for automated lumbar spine MRI analysis: A multi-pathology diagnostic agreement study.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 · 2026Article
- Intervertebral disc degeneration.Nature reviews. Disease primers · 2026Review
- Article
- Text-guided multimodal deep learning in magnetic resonance imaging for spinal structures segmentation and lumbar abnormalities identification.Quantitative imaging in medicine and surgery · 2025Article
- [Current Landscape and Commercialization of AI Models in Musculoskeletal Imaging].Journal of the Korean Society of Radiology · 2025Review
- Quantification and classification of lumbar disc herniation on axial magnetic resonance images using deep learning models.La Radiologia medica · 2025Article
- A machine learning based quantification system for automated diagnosis of lumbar spondylolisthesis on spinal X-rays.Heliyon · 2024Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Artificial intelligence: a new cutting-edge tool in spine surgery.Asian spine journal · 2024Article
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- A neural network model for detection and classification of lumbar spinal stenosis on MRI.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 · 2024Article
- How AI May Transform Musculoskeletal Imaging.Radiology · 2024Review
- MRI to digital medicine diagnosis: integrating deep learning into clinical decision-making for lumbar degenerative diseases.Frontiers in surgery · 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
- Deep learning-based detection and classification of lumbar disc herniation on magnetic resonance images.JOR spine · 2023Article
- Editorial on Special Issue "Spine Imaging: Novel Image Acquisition Techniques and Analysis Tools".Diagnostics (Basel, Switzerland) · 2022Article
- Automatic Grading of Disc Herniation, Central Canal Stenosis and Nerve Roots Compression in Lumbar Magnetic Resonance Image Diagnosis.Frontiers in endocrinology · 2022Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our objective was to evaluate the diagnostic performance of a convolutional neural network (CNN) trained on multiple MR imaging features of the lumbar spine, to detect a variety of different degenerative changes of the lumbar spine. One hundred and forty-six consecutive patients underwent routine clinical MRI of the lumbar spine including T2-weighted imaging and were retrospectively analyzed using a CNN for detection and labeling of vertebrae, disc segments, as well as presence of disc herniation, disc bulging, spinal canal stenosis, nerve root compression, and spondylolisthesis. The assessment of a radiologist served as the diagnostic reference standard. We assessed the CNN's diagnostic accuracy and consistency using confusion matrices and McNemar's test. In our data, 77 disc herniations (thereof 46 further classified as extrusions), 133 disc bulgings, 35 spinal canal stenoses, 59 nerve root compressions, and 20 segments with spondylolisthesis were present in a total of 888 lumbar spine segments. The CNN yielded a perfect accuracy score for intervertebral disc detection and labeling (100%), and moderate to high diagnostic accuracy for the detection of disc herniations (87%; 95% CI: 0.84, 0.89), extrusions (86%; 95% CI: 0.84, 0.89), bulgings (76%; 95% CI: 0.73, 0.78), spinal canal stenoses (98%; 95% CI: 0.97, 0.99), nerve root compressions (91%; 95% CI: 0.89, 0.92), and spondylolisthesis (87.61%; 95% CI: 85.26, 89.21), respectively. Our data suggest that automatic diagnosis of multiple different degenerative changes of the lumbar spine is feasible using a single comprehensive CNN. The CNN provides high diagnostic accuracy for intervertebral disc labeling and detection of clinically relevant degenerative changes such as spinal canal stenosis and disc extrusion of the lumbar spine.
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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.