• Neurosurgery · Jan 2024

    Review

    Machine Learning in Spine Surgery: A Narrative Review.

    • Samuel Adida, Andrew D Legarreta, Joseph S Hudson, David McCarthy, Edward Andrews, Regan Shanahan, Suchet Taori, Raj Swaroop Lavadi, Thomas J Buell, D Kojo Hamilton, Nitin Agarwal, and Peter C Gerszten.
    • Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA.
    • Neurosurgery. 2024 Jan 1; 94 (1): 536453-64.

    AbstractArtificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.Copyright © Congress of Neurological Surgeons 2023. All rights reserved.

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