Machine Learning in Spine Surgery: A Narrative Review

Author:

Adida Samuel1ORCID,Legarreta Andrew D.1,Hudson Joseph S.1,McCarthy David1,Andrews Edward1,Shanahan Regan1ORCID,Taori Suchet1ORCID,Lavadi Raj Swaroop1ORCID,Buell Thomas J.1,Hamilton D. Kojo1,Agarwal Nitin12,Gerszten Peter C.1

Affiliation:

1. Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA;

2. Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA

Abstract

Artificial 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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Neurology (clinical),Surgery

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