ArticleWorld neurosurgery2019
Machine Learning for the Prediction of Cervical Spondylotic Myelopathy: A Post Hoc Pilot Study of 28 Participants.
Article in World neurosurgery, 2019. 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 23 papers, 2 of them syntheses 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.
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
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 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
- Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.Neurosurgical review · 2022Pooled it
- Degenerative cervical myelopathy diagnosis: applicability of artificial intelligence-enhanced modalities in a systematic review and meta-analysis.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
- Resting-State Magnetoencephalography Functional Connectivity in Cervical Spondylotic Myelopathy: An MEG Study with SHAP-Based Interpretation.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- Application and performance of deep learning models for the automated diagnosis of cervical central spinal stenosis on MRI: a systematic review.Brain & spine · 2026Review
- Recent progress in surgical treatment of cervical spine myelopathy - A narrative review.Journal of clinical orthopaedics and trauma · 2025Article
- Constructing a nomogram model for patients with cervical spondylotic myelopathy.Scientific reports · 2025Article
- A severity classification model of cervical spondylotic radiculopathy symptoms based on MRI radiomics: A retrospective study.PloS one · 2025Article
- Convolutional Neural Networks in the Diagnosis of Cervical Myelopathy.Revista brasileira de ortopedia · 2024Article
- Automated Detection of Cervical Spinal Stenosis and Cord Compression via Vision Transformer and Rules-Based Classification.AJNR. American journal of neuroradiology · 2024Article
- Artificial Intelligence and Machine Learning Applications in Spine Surgery.International journal of spine surgery · 2023Article
- Forecast of pain degree of lumbar disc herniation based on back propagation neural network.Open life sciences · 2023Article
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Automatic Localization and Brand Detection of Cervical Spine Hardware on Radiographs Using Weakly Supervised Machine Learning.Radiology. Artificial intelligence · 2022Article
- Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms.Frontiers in surgery · 2022Article
- Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods.Brain & spine · 2022Review
- Article
- Feasibility of Deep Learning Algorithms for Reporting in Routine Spine Magnetic Resonance Imaging.International journal of spine surgery · 2020Article
- A Review on the Use of Artificial Intelligence in Spinal Diseases.Asian spine journal · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundCervical spondylotic myelopathy (CSM) severity and presence of symptoms are often difficult to predict based simply on clinical imaging alone. Similarly, improved machine learning techniques provide new tools with immense clinical potential.
methodsA total of 14 patients with CSM and 14 controls underwent imaging of the cervical spine. Two different artificial neural network models were trained; 1) to predict CSM diagnosis; and 2) to predict CSM severity. Model 1 consisted of 6 inputs including 3 common imaging scales for the evaluation of cord compression, alongside 3 objective magnetic resonance imaging measurements. The outcome for model 1 was binary to predict CSM diagnosis. Model 2 consisted of 23 input variables derived from probabilistic volume mapping measurements of white matter tracts in the region of compression. The outcome of model 2 was linear, to predict the modified Japanese Orthopedic Association (mJOA) score.
resultsModel 1 was used in predicting CSM. The mean cross-validated accuracy of the trained model was 86.50% (95% confidence interval, 85.16%-87.83%) with a median accuracy of 90.00%. Area under the curve (AUC) was calculated for each repetition. Average AUC for each repetition was 0.947 with a median AUC of 1.0. Average sensitivity, specificity, positive predictive value, and negative predictive value were 90.25%, 85.05%, 81.58%, and 91.94%, respectively. Model 2 was used in modeling mJOA. The mJOA model predicted scores, with a mean and median error of -0.29 mJOA points and -0.08 mJOA points, respectively, mean error per batch was 0.714 mJOA points.
conclusionsMachine learning provides a promising method for prediction, diagnosis, and even prognosis in patients with CSM.
Indexed as
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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.