ArticleScientific reports2021
A deep learning model for detection of cervical spinal cord compression in MRI scans.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05099627 (A Point-of-care Machine Learning Model and Scoring System for the Early Diagnosis and Prediction of Treatment Outcome in Patients With Cervical Myelopathy), which is not on this map. Cited by 39 papers, 2 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.
A Point-of-care Machine Learning Model and Scoring System for the Early Diagnosis and Prediction of Treatment Outcome in Patients With Cervical Myelopathy: an Ambispective Cohort Study.
Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 80 citations in OpenAlex.
- Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Radiological predictors of shunt response in the diagnosis and treatment of idiopathic normal pressure hydrocephalus: a systematic review and meta-analysis.Acta neurochirurgica · 2023Pooled it
- Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening.Journal of healthcare engineering · 2022Trial
- ShuffleLeNet architecture for spinal cord injury classification and level detection.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
- Development and multi-institutional validation of a deep learning algorithm for predicting cervical cord compression using dynamic cervical lateral radiographs.Scientific reports · 2026Article
- Article
- ShPCFHNet: shepherd parallel convolutional forward harmonic net for spinal cord injury detection using CT images.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
- Enhancing Spinal Cord and Canal Segmentation in Degenerative Cervical Myelopathy : The Role of Interactive Learning Models with manual Click.Journal of Korean Neurosurgical Society · 2026Article
- Application and performance of deep learning models for the automated diagnosis of cervical central spinal stenosis on MRI: a systematic review.Brain & spine · 2026Review
- Automatic and observable spinal cord compression grading system for lung cancer spinal metastasis.Quantitative imaging in medicine and surgery · 2025Article
- Deep learning-based prediction of cervical canal stenosis from mid-sagittal T2-weighted MRI.Skeletal radiology · 2025Article
- A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation.Scientific reports · 2025Article
- Future perspectives after the guidelines of degenerative cervical myelopathy: A narrative review.Journal of clinical orthopaedics and trauma · 2025Article
- Feasibility of fully automatic assessment of cervical canal stenosis using MRI via deep learning.Quantitative imaging in medicine and surgery · 2025Article
- An efficient deep learning based approach for automated identification of cervical vertebrae fracture as a clinical support aid.Scientific reports · 2025Article
- Deep-Learning-Based Computer-Aided Grading of Cervical Spinal Stenosis from MR Images: Accuracy and Clinical Alignment.Bioengineering (Basel, Switzerland) · 2025Article
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- Fractal-Based Architectures with Skip Connections and Attention Mechanism for Improved Segmentation of MS Lesions in Cervical Spinal Cord.Diagnostics (Basel, Switzerland) · 2025Article
- Quantitative MRI Assessment of Post-Surgical Spinal Cord Injury Through Radiomic Analysis.Journal of imaging · 2024Article
- Using Artificial Intelligence in the Comprehensive Management of Spinal Cord Injury.Korean journal of neurotrauma · 2024Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volume of medical imaging data and provide initial interpretation of images gathered in a primary-care setting. We aimed to develop and validate a deep learning model for detection of cervical spinal cord compression in MRI scans. Patients undergoing surgery for DCM as a part of the AO Spine CSM-NA or CSM-I prospective cohort studies were included in our study. Patients were divided into a training/validation or holdout dataset. Images were labelled by two specialist physicians. We trained a deep convolutional neural network using images from the training/validation dataset and assessed model performance on the holdout dataset. The training/validation cohort included 201 patients with 6588 images and the holdout dataset included 88 patients with 2991 images. On the holdout dataset the deep learning model achieved an overall AUC of 0.94, sensitivity of 0.88, specificity of 0.89, and f1-score of 0.82. This model could improve the efficiency and objectivity of the interpretation of cervical spine MRI scans.
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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.