ReviewEFORT open reviews2024
Advancing spine care through AI and machine learning: overview and applications.
Review in EFORT open reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed.
- Artificial Intelligence for Clinical Decision Support in Rural Spine Care: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Artificial intelligence in spine care: A scoping review of treatment applications.North American Spine Society journal · 2026Review
- Deep learning-based estimation of sagittal spinal alignment from coronal radiographs.Frontiers in bioengineering and biotechnology · 2026Article
- From mechanotransduction to manual therapy: advances in piezo/TRP channels and lumbar degeneration.Frontiers in physiology · 2026Review
- Simulating Federated Learning to Enable Multi-Hospital Collaboration for Lumbopelvic Alignment Estimation.JOR spine · 2025Article
- Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.Bioengineering (Basel, Switzerland) · 2025Review
- Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review.Brain & spine · 2025Review
- Multi-Omics Research on Angina Pectoris: A Novel Perspective.Aging and disease · 2024Review
- Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters.Frontiers in nutrition · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML), a subset of artificial intelligence, is crucial for spine care and research due to its ability to improve treatment selection and outcomes, leveraging the vast amounts of data generated in health care for more accurate diagnoses and decision support. ML's potential in spine care is particularly notable in radiological image analysis, including the localization and labeling of anatomical structures, detection and classification of radiological findings, and prediction of clinical outcomes, thereby paving the way for personalized medicine. The manuscript discusses ML's application in spine care, detailing supervised and unsupervised learning, regression, classification, and clustering, and highlights the importance of both internal and external validation in assessing ML model performance. Several ML algorithms such as linear models, support vector machines, decision trees, neural networks, and deep convolutional neural networks, can be used in the spine domain to analyze diverse data types (visual, tabular, omics, and multimodal).
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.