ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026
Artificial intelligence and machine learning based predictive analytics in cervical myelopathy - a scoping review.
Article in 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, 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.
- Prediction models for progression and outcomes in degenerative cervical myelopathy: a systematic review and critical appraisal.Frontiers in neurology · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the current landscape of artificial intelligence (AI) and machine learning (ML) applications in predictive analytics for cervical myelopathy, focusing on diagnostic accuracy, patient stratification, surgical decision-making, and outcome prediction.
methodsA systematic search was conducted across PubMed, Embase, Scopus, and Web of Science using keywords related to cervical myelopathy and AI/ML. Studies were selected based on relevance to AI-driven predictive models in diagnosis, prognosis, and management of cervical myelopathy. Data extraction included algorithm types, validation methods, clinical applicability, and performance metrics. Qualitative synthesis was performed to identify thematic trends and methodological gaps.
resultsOut of 169 records, 41 studies met inclusion criteria. AI/ML models demonstrated high accuracy in gait analysis, hand motion tracking, and image-based diagnostics, with some achieving > 90% sensitivity and specificity. ML algorithms effectively stratified patients into phenotypic clusters and predicted surgical outcomes, including postoperative complications and opioid use. Radiomics and deep learning enhanced imaging interpretation, while ensemble models improved prognostic reliability. Despite promising results, limitations included data heterogeneity, retrospective design, and lack of standardised validation protocols.
conclusionsAI and ML are reshaping cervical myelopathy management by enabling precise diagnostics, personalised treatment planning, and predictive modelling. Integration of multimodal data and explainable AI frameworks is essential for clinical adoption. Future research should prioritise prospective validation, ethical safeguards, and interdisciplinary collaboration to bridge the gap between technological innovation and patient-centred care.
Indexed as
Identifiers
41680316What OpenQuestion holds
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.