ArticleGlobal spine journal2021
Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions.
Article in Global spine journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 26 citations in OpenAlex.
- Review
- CT Evaluation of Lumbar Interbody Fusion: A Comprehensive Review with an Integrated Framework for Principle-Based Interpretation.Diagnostics (Basel, Switzerland) · 2026Review
- Advancing medical imaging diagnostics using deep learning for accurate spinal disorder classification.Journal of education and health promotion · 2026Article
- Review
- 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
- Clinical Decision Support System Used in Spinal Disorders: Scoping Review.Journal of medical Internet research · 2024Article
- Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions.PloS one · 2024Article
- Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images.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 · 2023Article
- Review
- Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI.Frontiers in oncology · 2022Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designNarrative review.
objectivesWe aim to describe current progress in the application of artificial intelligence and machine learning technology to provide automated analysis of imaging in patients with spinal disorders.
methodsA literature search utilizing the PubMed database was performed. Relevant studies from all the evidence levels have been included.
resultsWithin spine surgery, artificial intelligence and machine learning technologies have achieved near-human performance in narrow image classification tasks on specific datasets in spinal degenerative disease, spinal deformity, spine trauma, and spine oncology.
conclusionAlthough substantial challenges remain to be overcome it is clear that artificial intelligence and machine learning technology will influence the practice of spine surgery in the future.
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.