ArticleAsian spine journal2020
A Review on the Use of Artificial Intelligence in Spinal Diseases.
Article in Asian spine journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses 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
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 61 citations in OpenAlex.
- Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.BMC surgery · 2024Pooled it
- Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- Low Back Pain Among Health Sciences Undergraduates: Results Obtained from a Machine-Learning Analysis.Journal of clinical medicine · 2025Article
- The Application of Deep Learning to Accurately Identify the Dimensions of Spinal Canal and Intervertebral Foramen as Evaluated by the IoU Index.Bioengineering (Basel, Switzerland) · 2024Article
- Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning-based quantitative morphological study of anteroposterior digital radiographs of the lumbar spine.Quantitative imaging in medicine and surgery · 2024Article
- Clinical Decision Support System Used in Spinal Disorders: Scoping Review.Journal of medical Internet research · 2024Article
- Current Trends in Artificial Intelligence-Assisted Spine Surgery: A Systematic Review.Asian spine journal · 2024Article
- Predicting Survival in Glioblastoma Using Gene Expression Databases: A Neural Network Analysis.International journal of molecular and cellular medicine · 2024Article
- MRI to digital medicine diagnosis: integrating deep learning into clinical decision-making for lumbar degenerative diseases.Frontiers in surgery · 2024Article
- The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications.North American Spine Society journal · 2023Article
- Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning.Journal of clinical medicine · 2023Review
- An approach to the diagnosis of lumbar disc herniation using deep learning models.Frontiers in bioengineering and biotechnology · 2023Article
- Comparison of Concordance between Chuna Manual Therapy Diagnostic Methods (Palpation, X-ray, Artificial Intelligence Program) in Lumbar Spine: An Exploratory, Cross-Sectional Clinical Study.Diagnostics (Basel, Switzerland) · 2022Article
- Comparison of manual versus automated measurement of Cobb angle in idiopathic scoliosis based on a deep learning keypoint detection technology.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 · 2022Article
- Novel Magnetic Resonance Imaging Tools for the Diagnosis of Degenerative Disc Disease: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- AI MSK clinical applications: spine imaging.Skeletal radiology · 2022Review
- The value of quantitative magnetic resonance imaging signal intensity in distinguishing between spinal meningiomas and schwannomas.International journal of medical sciences · 2022Article
- Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative TJOR spine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: "artificial neural networks," "spine," "back pain," "prognosis," "grading," "classification," "prediction," "segmentation," "biomechanics," "deep learning," and "imaging." The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.
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.