An Introduction to Machine Learning for the Practicing Spine Surgeon
The applications of new and emerging technologies in spine surgery are constantly expanding. Specifically, machine learning algorithms have seen a rise in utilization in clinical research, allowing for interpretation of large datasets that have the capability of experiential learning. The goal of this work is to present a guide for surgeons to better understand model design, key takeaways, and common pitfalls related to machine learning to ensure accurate and appropriate interpretation of analytical findings in their practice.
Authors
- Ruchir Nanavati (ORCID: https://orcid.org/0009-0008-5282-0808)
- Jarod Olson (ORCID: https://orcid.org/0000-0002-3523-1680)
- Robert J. Oris (ORCID: https://orcid.org/0000-0001-8743-0860)
- Ryan T. Lin
- Barrett I. Woods
- Jonathan Dalton
- Mark Miller (ORCID: https://orcid.org/0009-0005-4936-2832)
- Matthew H. Meade
- Gregory D. Schroeder
- Alexander R. Vaccaro
- Joydeep Baidya
Institutions
- Thomas Jefferson University Hospital (US)
- University of Pittsburgh Medical Center (US)
- Rothman Institute (US)
Publication Details
- Journal
- Clinical Spine Surgery A Spine Publication
- Published
- 2026-01-05
- DOI
- https://doi.org/10.1097/bsd.0000000000002026
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00