ReviewFrontiers in surgery2020
The Role of Machine Learning in Spine Surgery: The Future Is Now.
Review in Frontiers in surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 107 citations in OpenAlex.
- Artificial intelligence applications in surgical education and training: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- State-of-the-Art of Non-Radiative, Non-Visual Spine Sensing with a Focus on Sensing Forces, Vibrations and Bioelectrical Properties: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules.BMC medical informatics and decision making · 2024Trial
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- Predicting Surgical Site Infection after Lumbar Laminectomy and Discectomy: A Cutting-edge Algorithmic Approach by Incorporating Ensembled Stacking into the Current State-of-the-art for Automated Machine Learning.Neurosurgical review · 2025Article
- MRI Radiomics based on paraspinal muscle for prediction postoperative outcomes in lumbar degenerative spondylolisthesis.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
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- AI and machine learning in paediatric spine deformity surgery.Bone & joint open · 2025Article
- Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning.Scientific reports · 2025Article
- YOLOv8-Seg: a deep learning approach for accurate classification of osteoporotic vertebral fractures.Frontiers in radiology · 2025Article
- Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.Spine deformity · 2025Article
- Optimizing neurosurgical operating room schedule: Integrating machine learning models for operative time prediction and schedule design.Surgical neurology international · 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
- Machine Learning Models Leveraging Smartphone-Based Patient Mobility Data Can Accurately Predict Functional Outcomes After Spine Surgery.Journal of clinical medicine · 2024Article
- Convolutional Neural Networks in the Diagnosis of Cervical Myelopathy.Revista brasileira de ortopedia · 2024Article
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Application of Artificial Intelligence in Shoulder Pathology.Diagnostics (Basel, Switzerland) · 2024Review
- 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
- Differentiation of Native Vertebral Osteomyelitis: A Comprehensive Review of Imaging Techniques and Future Applications.Medical science monitor : international medical journal of experimental and clinical research · 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
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over- or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically.
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.