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Current Applications of Machine Learning in Spine: From Clinical View
GuanRui Ren1, Kun Yu2, ZhiYang Xie3
1Southeast University Medical College, Nanjing, Jiangsu, China.
Global Spine Journal
|October 11, 2021
Summary
Machine learning (ML) shows great promise in spine care, improving diagnostics, surgical assistance, and patient outcomes. Further research is needed for widespread clinical adoption.
Area of Science:
- Spine Medicine
- Medical Informatics
- Artificial Intelligence
Background:
- Machine learning (ML) is increasingly utilized in various medical fields.
- The spine domain presents unique challenges and opportunities for ML applications.
Purpose of the Study:
- To review current applications of machine learning (ML) in the spine domain for clinicians.
- To summarize the key findings and potential impact of ML in spine care.
Main Methods:
- A comprehensive PubMed search was conducted for articles published between 2006 and 2020.
- Keywords included 'spine', 'spinal', 'lumbar', 'cervical', 'thoracic', and 'machine learning'.
- Studies were screened, excluding those outside the spine domain, case reports, reviews, meta-analyses, and articles lacking abstracts or full text.
Main Results:
- Out of 1738 retrieved articles, 292 studies were included in the review.
- ML applications span image processing, diagnosis, decision support, operative assistance, and rehabilitation.
- Key areas of impact include surgery outcomes, complication prediction, hospitalization, and cost reduction.
Conclusions:
- Machine learning demonstrates excellent performance and significant potential in spine care.
- ML can enhance clinical decision-making, improve efficiency, and reduce adverse events.
- Further randomized controlled trials and improved model interpretability are crucial for clinical acceptance.
