ArticleNature communications2022
Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 3 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
68 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence automated measurements of spinopelvic parameters in adult spinal deformity-a systematic review.Spine deformity · 2025Pooled it
- Deep learning assisted segmentation of the lumbar intervertebral disc: a systematic review and meta-analysis.Journal of orthopaedic surgery and 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
- EADC-Net: A Dual-Stage Deep Learning Framework with Spatial and Channel Central Attention for Automated Detection of Lumbar Degenerative Disc Disease from Radiographs.Journal of imaging informatics in medicine · 2026Article
- Data and knowledge-driven imaging biomarkers for lumbar aging and degenerative risk stratification monitoring.NPJ digital medicine · 2026Article
- CEST MRI: Translational prospects for intervertebral disc degeneration - from basic research to clinical applications.Journal of orthopaedic translation · 2026Review
- Clinical evaluation of deep learning accelerated lumbar T2-weighted and fat-suppressed MRI sequences.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
- Review
- Intelligent prediction of cervical instability based on MRI radiomics combined with clinical features.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
- Review
- 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
- SpineScan: a deep learning model for lumbar spine MRI annotation and Pfirrmann grading assessment.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
- Advances and challenges in AI-assisted MRI for lumbar disc degeneration detection and classification.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 · 2026Review
- Automated diagnostic of cervical spondylosis on multimodal medical images with a multi-task deep learning model.Nature communications · 2026Article
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Clinical Application of Deep Learning for Spine MRI Interpretation: A Multicenter Evaluation of Artificial-Intelligence-Assisted versus Manual Reading on Diagnostic Agreement with the Reference Standard.Research (Washington, D.C.) · 2026Article
- Automated radiomics pipeline integrating TotalSpineSeg for Pfirrmann grading of lumbar disc degeneration with SHAP-based interpretability.Frontiers in medicine · 2026Article
- Deep learning-radiomics assessment of intervertebral disc and paraspinal muscle heterogeneity for predicting postoperative recurrent lumbar disc herniation.Frontiers in artificial intelligence · 2026Article
- Size-optimized ceria nanoparticles attenuate intervertebral disc degeneration by rescuing nucleus pulposus cell senescence via reducing oxidative stress and stimulating PI3K/AKT pathway.Materials today. Bio · 2025Article
- Evaluation of Lumbar Intervertebral Disc and Facet Joint Degeneration Using Histogram Analysis of T2 and T2* Values.JOR spine · 2025Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To help doctors and patients evaluate lumbar intervertebral disc degeneration (IVDD) accurately and efficiently, we propose a segmentation network and a quantitation method for IVDD from T2MRI. A semantic segmentation network (BianqueNet) composed of three innovative modules achieves high-precision segmentation of IVDD-related regions. A quantitative method is used to calculate the signal intensity and geometric features of IVDD. Manual measurements have excellent agreement with automatic calculations, but the latter have better repeatability and efficiency. We investigate the relationship between IVDD parameters and demographic information (age, gender, position and IVDD grade) in a large population. Considering these parameters present strong correlation with IVDD grade, we establish a quantitative criterion for IVDD. This fully automated quantitation system for IVDD may provide more precise information for clinical practice, clinical trials, and mechanism investigation. It also would increase the number of patients that can be monitored.
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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.