ArticleJournal of healthcare engineering2021
Deep Learning-Based Denoised MRI Images for Correlation Analysis between Lumbar Facet Joint and Lumbar Disc Herniation in Spine Surgery.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis 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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.BMC surgery · 2024Pooled it
- Impact of fixed-point rotational reduction of the spine on pain scores and lower limb motor function in patients with lumbar facet joint disorder.BMC musculoskeletal disorders · 2025Trial
- 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
- Quantification and classification of lumbar disc herniation on axial magnetic resonance images using deep learning models.La Radiologia medica · 2025Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work aimed to explore the relationship between spine surgery lumbar facet joint (LFJ) and lumbar disc herniation (LDH) via compressed sensing algorithm-based MRI images to analyze the clinical symptoms of patients with residual neurological symptoms after LDH. Under weighted BM3D denoising, Epigraph method was introduced to establish the novel CSMRI reconstruction algorithm (BEMRI). 127 patients with LDH were taken as the research objects. The BEMRI algorithm was compared with others regarding peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Patients' bilateral LFJ angles were compared. The relationships between LFJ angles, lumbar disc degeneration, and LFJ degeneration were analyzed. It turned out that the PSNR and SSIM of BEMRI algorithm were evidently superior to those of other algorithms. The proportion of patients with grade IV degeneration was at most 31.76%. Lumbar disc grading was positively correlated with change grading of LFJ degeneration (
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Registered trials
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