SynthesisJournal of orthopaedic surgery and research2024
Deep learning assisted segmentation of the lumbar intervertebral disc: a systematic review and meta-analysis.
Synthesis in Journal of orthopaedic surgery and research, 2024. 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.
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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.
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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 for the diagnosis of lumbar disc herniation: a systematic review and meta-analysis.BMC medical imaging · 2026Pooled it
- Multicenter evaluation of four large language models for automated spine imaging diagnosis.NPJ digital medicine · 2026Article
- Balancing accuracy and efficiency in lumbar spine image segmentation using multi-scale attention and residual learning.Scientific reports · 2026Article
- Medical Spine Sagittal MRI Dataset for Segmentation and Foraminal Stenosis detection.Scientific data · 2026Article
- Deep Learning-Based Segmentation in Musculoskeletal Imaging: A Review of Research Trends.Journal of the Korean Society of Radiology · 2025Review
- Intervertebral disc anomaly intelligent classification system based on deep learning, IDAICS.Frontiers in radiology · 2025Article
- Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn recent years, deep learning (DL) technology has been increasingly used for the diagnosis and treatment of lumbar intervertebral disc (IVD) degeneration. This study aims to evaluate the performance of DL technology for IVD segmentation in magnetic resonance (MR) images and explore improvement strategies.
methodsWe developed a PRISMA systematic review protocol and systematically reviewed studies that used DL algorithm frameworks to perform IVD segmentation based on MR images published up to April 10, 2024. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess methodological quality, and the pooled dice similarity coefficient (DSC) score and Intersection over Union (IoU) were calculated to evaluate segmentation performance.
results45 studies were included in this systematic review, of which 16 provided complete segmentation performance data and were included in the quantitative meta-analysis. The results indicated that DL models showed satisfactory IVD segmentation performance, with a pooled DSC of 0.900 (95% confidence interval [CI]: 0.887-0.914) and IoU of 0.863 (95% CI: 0.730-0.995). However, the subgroup analysis did not show significant effects of factors on IVD segmentation performance, including network dimensionality, algorithm type, publication year, number of patients, scanning direction, data augmentation, and cross-validation.
conclusionsThis study highlights the potential of DL technology in IVD segmentation and its further applications. However, due to the heterogeneity in algorithm frameworks and result reporting of the included studies, the conclusions should be interpreted with caution. Future research should focus on training generalized models on large-scale datasets to enhance their clinical application.
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