ReviewBrain & spine2026
Application and performance of deep learning models for the automated diagnosis of cervical central spinal stenosis on MRI: a systematic review.
Review in Brain & spine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Cervical central spinal stenosis (CCSS) is a leading cause of adult spinal cord dysfunction. Magnetic resonance imaging (MRI) is the reference standard, but reporting is time-consuming and subject to inter-observer variability. Artificial intelligence (AI)-especially deep-learning-may enable automated, consistent assessment. Research question: Evaluation of performance metrics of AI models for diagnosing CCSS. Material and methods: Following PRISMA 2020, we searched PubMed, Cochrane, Embase, IEEE Xplore, and Web of Science (2015-July 2025) for studies training and evaluating AI models using MRI to diagnose or grade CCSS. We excluded studies limited to foraminal stenosis, non-MRI modalities, thoracic/lumbar levels, segmentation-only or image-enhancement tools without diagnostic output, and studies focused solely on non-stenotic cervical pathologies. Data were extracted on MRI protocol, model type, data splits and external validation, stenosis classification, and diagnostic performance. Results: Ten studies (2019-2025) met inclusion criteria, predominantly single-centre and retrospective. Most models used T2-weighted axial and/or sagittal MRI; CNNs (e.g., ResNet-50, EfficientNet) and Transformer-based architectures were common. Sensitivities ranged roughly 0.67-1.00 and specificities 0.42-0.97 across models, with many reporting AUCs ≥0.90 and accuracies ≥0.85. Only one study reported true external test performance. Reporting of confidence intervals, processing time, and explainability (e.g., Grad-CAM) was inconsistent. Discussion and conclusion: Deep-learning shows promising diagnostic performance for automated CCSS assessment on MRI and could reduce variability and reporting time. However, generalisability remains uncertain due to small, retrospective, largely single-centre cohorts and scarce external validation. Standardized reporting (e.g., CLAIM) and prospective, multi-centre validation is needed before routine clinical deployment.
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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.