ReviewGlobal spine journal2022
Current Applications of Machine Learning in Spine: From Clinical View.
Review in Global spine journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning assisted segmentation of the lumbar intervertebral disc: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2024Pooled it
- Machine learning identifies distinct movement control impairment clusters in patients with chronic neck pain.Scientific reports · 2026Article
- Interpretable prediction of prolonged length of stay for patients undergoing lumbar disc herniation surgery based on machine learning and SHAP.BMC health services research · 2026Article
- Advancing medical imaging diagnostics using deep learning for accurate spinal disorder classification.Journal of education and health promotion · 2026Article
- Radiologist-Validated Automatic Lumbar T1-Weighted Spinal MRI Segmentation Tool via an Attention U-Net Algorithm.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation.World journal of orthopedics · 2025Review
- Vision transformer-based diagnosis of lumbar disc herniation with grad-CAM interpretability in CT imaging.BMC musculoskeletal disorders · 2025Article
- Low Back Pain Among Health Sciences Undergraduates: Results Obtained from a Machine-Learning Analysis.Journal of clinical medicine · 2025Article
- Interpretable Machine Learning Model to Predict Bone Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fracture Based on SHapley Additive exPlanations.Global spine journal · 2025Article
- Machine Learning Prediction Model and Risk Factor Analysis of Reoperation in Recurrent Lumbar Disc Herniation Patients After Percutaneous Endoscopic Lumbar Discectomy.Global spine journal · 2024Article
- A deep learning approach for cervical cord injury severity determination through axial and sagittal magnetic resonance imaging segmentation and classification.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 · 2024Article
- Artificial Intelligence and Machine Learning in Spine Research: A New Frontier.Bioengineering (Basel, Switzerland) · 2024Article
- A high-quality dataset featuring classified and annotated cervical spine X-ray atlas.Scientific data · 2024Article
- Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024Review
- Reference Data for Diagnosis of Spondylolisthesis and Disc Space Narrowing Based on NHANES-II X-rays.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
- Clinical Decision Support System Used in Spinal Disorders: Scoping Review.Journal of medical Internet research · 2024Article
- Using Augmented Reality Technology to Optimize Transfacet Lumbar Interbody Fusion: A Case Report.Journal of clinical medicine · 2024Article
- Automated Detection of Cervical Spinal Stenosis and Cord Compression via Vision Transformer and Rules-Based Classification.AJNR. American journal of neuroradiology · 2024Article
- Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis.Journal of clinical medicine · 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
STUDY
designNarrative review.
objectivesThis review aims to present current applications of machine learning (ML) in spine domain to clinicians.
methodsWe conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text.
resultsTotal 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost.
conclusionsML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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.