Abstract
The complexity of patients with spine pathology and high rates of complications has driven extensive research directed toward optimizing outcomes and reducing complications. Traditional statistical analysis has been limited both in validity and in the number of predictor variables considered. Over the past decade, artificial intelligence and machine learning have taken center stage as the possible solution to creating more accurate and applicable patient-centered predictive models in spine surgery. This review discusses the current published machine learning applications on preoperative optimization, risk stratification, and predictive modeling for the cervical, lumbar, and adult spinal deformity populations.
- machine learning
- artificial intelligence
- cervical spine
- lumbar spine
- adult spinal deformity
- predictive model
Footnotes
Funding The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests The authors report no conflicts of interest in this work.
Disclosures Nathan John Lee has nothing to report. Joseph M. Lombardi reports consulting fees from Medtronic and Stryker and stock/stock options from OnPoint Surgical. Ronald A. Lehman Jr reports grants/contracts from the Department of Defense and royalties/licenses and patents from Medtronic and Stryker.
- This manuscript is generously published free of charge by ISASS, the International Society for the Advancement of Spine Surgery. Copyright © 2023 ISASS. To see more or order reprints or permissions, see http://ijssurgery.com.