SynthesisNeurosurgical review2022
Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.
Synthesis in Neurosurgical review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Evaluating tissue injury in cervical spondylotic myelopathy with spinal cord MRI: a systematic review.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 · 2024Pooled it
- Robot-assisted surgery for degenerative lumbar spine diseases: a disease-specific bibliometric and knowledge-mapping analysis.Journal of robotic surgery · 2026Article
- Intervertebral disc degeneration.Nature reviews. Disease primers · 2026Review
- Advancing medical imaging diagnostics using deep learning for accurate spinal disorder classification.Journal of education and health promotion · 2026Article
- CT-based machine learning radiomics modeling to screen for lumbar spine osteoporosis.Frontiers in medicine · 2026Article
- Advancing Precision Medicine in Degenerative Cervical Myelopathy.Journal of clinical medicine · 2025Review
- Effectiveness and reliability of AI in diagnosis and robot-assisted spinal and cranial surgery: efficient outcomes and ethical worries.Annals of medicine and surgery (2012) · 2025Review
- Machine learning can predict surgical indication: new clustering model from a large adult spine deformity database.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 · 2025Article
- The research progress and potential applications ofThe British journal of nutrition · 2025Review
- Comparative Effectiveness of Optimized Versus Conventional Perioperative Care in Unilateral Biportal Endoscopy for Lumbar Degenerative Disease: A Single-Center Retrospective Study.Journal of pain research · 2025Article
- Spinal cord demyelination predicts neurological deterioration in patients with mild degenerative cervical myelopathy.BMJ neurology open · 2025Article
- Artificial intelligence in predicting postoperative heterotopic ossification following anterior cervical disc replacement.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
- The strategic use of Big Data - A study protocol for a multicenter clinical trial testing if the use of the Swespine Dialogue Support alter outcomes in degenerative spine surgery.BMC musculoskeletal disorders · 2024Article
- [Preliminary comparative study of spinal robot-assisted pedicle screw placement using different surgical approaches].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2024Article
- Determining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning.Global spine journal · 2024Article
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Clinical Decision Support System Used in Spinal Disorders: Scoping Review.Journal of medical Internet research · 2024Article
- Automated Detection of Cervical Spinal Stenosis and Cord Compression via Vision Transformer and Rules-Based Classification.AJNR. American journal of neuroradiology · 2024Article
- Artificial Intelligence and Machine Learning Applications in Spine Surgery.International journal of spine surgery · 2023Article
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 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning is a rapidly evolving field that offers physicians an innovative and comprehensive mechanism to examine various aspects of patient data. Cervical and lumbar degenerative spine disorders are commonly age-related disease processes that can utilize machine learning to improve patient outcomes with careful patient selection and intervention. The aim of this study is to examine the current applications of machine learning in cervical and lumbar degenerative spine disease. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search of PubMed, Embase, Medline, and Cochrane was conducted through May 31st, 2020, using the following terms: "artificial intelligence" OR "machine learning" AND "neurosurgery" AND "spine." Studies were included if original research on machine learning was utilized in patient care for degenerative spine disease, including radiographic machine learning applications. Studies focusing on robotic applications in neurosurgery, navigation, or stereotactic radiosurgery were excluded. The literature search identified 296 papers, with 35 articles meeting inclusion criteria. There were nine studies involving cervical degenerative spine disease and 26 studies on lumbar degenerative spine disease. The majority of studies for both cervical and lumbar spines utilized machine learning for the prediction of postoperative outcomes, with 5 (55.6%) and 15 (61.5%) studies, respectively. Machine learning applications focusing on degenerative lumbar spine greatly outnumber the current volume of cervical spine studies. The current research in lumbar spine also demonstrates more advanced clinical applications of radiographic, diagnostic, and predictive machine learning models.
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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.