ArticleOsteoarthritis and cartilage open2023
Automated segmentation and prediction of intervertebral disc morphology and uniaxial deformations from MRI.
Article in Osteoarthritis and cartilage open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning assisted segmentation of the lumbar intervertebral disc: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2024Pooled it
- Statistical shape modeling of MRI-based morphological response of lumbar intervertebral discs to a unilateral side lying spinal rotation mobilization.Osteoarthritis and cartilage open · 2026Article
- A deep learning-based approach for measuring patellar cartilage deformations from knee MR images.Journal of biomechanics · 2025Article
- Resting Supine for 45 Minutes Yields Consistent Baseline Lumbar Intervertebral Disc Height in Asymptomatic Participants.Annals of biomedical engineering · 2025Article
- Tibiofemoral cartilage strain and recovery following a 3-mile run measured using deep learning segmentation of bone and cartilage.Osteoarthritis and cartilage open · 2025Article
- Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students' and Physicians' Perceptions.JMIR medical education · 2023Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The measurement of Design: Therefore, we developed and evaluated two commonly employed deep learning architectures (2D and 3D U-Net) for the segmentation of IVDs from MRI. The performance of these models was evaluated for morphological accuracy by comparing predicted IVD segmentations (Dice similarity coefficient, mDSC; average surface distance, ASD) to manual (ground truth) measures. Likewise, functional reliability and precision were assessed by evaluating the intraclass correlation coefficient (ICC) and standard error of measurement (SE Results: Peak model performance was obtained using the 3D U-net architecture, yielding a maximum mDSC = 0.9824 and component-wise ASD Conclusions: This study demonstrated that a deep learning framework can precisely and reliably automate measures of IVD function, drastically improving the throughput of these time-intensive methods.
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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.