ReviewJournal of clinical medicine2024
Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating machine learning into acupuncture research: a scoping review.Frontiers in neurology · 2025Pooled it
- Early urodynamic profiles predict lower urinary tract recovery after spinal cord injury in female rats.Physiological reports · 2026Article
- Prediction of Clinically Significant Improvement after Lumbar Fusion Surgery Based on Machine Learning.Yonsei medical journal · 2026Article
- Severe trauma care: advances and future directions in diagnostic and therapeutic techniques and information technology support.Medical review (2021) · 2026Article
- Innovative Assistive Technologies for Tetraplegia: A Narrative Review of Systematic and Emerging Evidence.Healthcare (Basel, Switzerland) · 2026Review
- Advancing medical imaging diagnostics using deep learning for accurate spinal disorder classification.Journal of education and health promotion · 2026Article
- Accurate Segmentation and Three-dimensional Reconstruction Algorithm of Spinal Cord Injury Lesions Based on Multimodal Magnetic Resonance Imaging.Current medical imaging · 2026Article
- CRISPR and Artificial Intelligence in Neuroregeneration: Closed-Loop Strategies for Precision Medicine, Spinal Cord Repair, and Adaptive Neuro-Oncology.International journal of molecular sciences · 2025Review
- Artificial Intelligence and Internet of Things Integration in Pharmaceutical Manufacturing: A Smart Synergy.Pharmaceutics · 2025Review
- The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review.Brain & spine · 2025Review
- Closing the diagnostic gap: A narrative review of recent advances in functional MRI diagnostics in spinal cord injury.Brain & spine · 2025Review
- A risk prediction model for poor joint function recovery after ankle fracture surgery based on interpretable machine learning.Frontiers in medicine · 2025Article
- Transferable CNN-based data mining approaches for medical imaging: application to spine DXA scans for osteoporosis detection.Frontiers in computational neuroscience · 2025Article
- A Protocol for Comprehensive Analysis of Gait in Individuals with Incomplete Spinal Cord Injury.Methods and protocols · 2024Article
- Synergizing Deep Learning-Enabled Preprocessing and Human-AI Integration for Efficient Automatic Ground Truth Generation.Bioengineering (Basel, Switzerland) · 2024Article
- The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review.Patient safety in surgery · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.
Indexed as
Identifiers
What 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.