Abstract
The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over- or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically.
Highlights
In clinical medicine, the rise of machine learning applications represents a new era of solving healthcare problems
Examples of Machine Learning (ML) applications encountered by spine surgeons include image classification [i.e., automated detection of vertebral body compression fractures on CT or MRI [18,19,20]], preoperative risk stratification models, clinical decision support tools [21,22,23,24,25], among others
Varghese et al produced a promising model with very low error rates and an Area Under the Curve (AUC) of 1.00 for predicting pedicle screw failure, which was internally validated against a separate set of novel permutations (Figure 6) [34]
Summary
The rise of machine learning applications represents a new era of solving healthcare problems. Examples of ML applications encountered by spine surgeons include image classification [i.e., automated detection of vertebral body compression fractures on CT or MRI [18,19,20]], preoperative risk stratification models, clinical decision support tools [21,22,23,24,25], among others.
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