The diagnostic and prognostic value of artificial intelligence and artificial neural networks in spinal surgery

Abstract

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article: Bone Joint J 2021;103-B(9):1442–1448.

Artificial intelligence and computer vision in orthopaedic trauma

“…[20][21][22] there has been a considerable increase in the number of studies aiming to improve clinical decision-making through the analysis of large databases using aI and computer vision. 18,19,23 the next phase should focus on prospective clinical evaluation, the maturation of techniques, and expansion of work to gain external validity in geographical areas and populations, in order to consolidate accuracy, reliability, and transferability while minimizing bias. 19 Kunze et al 24 and others have emphasized these factors and the need for improvement in the regulations and standards for taxonomy, the quality of data, critical appraisal, and reporting.…”

Looking back over the past year

“…[2][3][4][5][6] Their message should be heeded by all readers, regardless of their subspecialty. [7][8][9][10] If we are to see benefits for our patients, the questions addressed must be meaningful, the methodology must be rigorous, and the results must be reported transparently, appropriate data must be available, and each model of AI must be externally validated. The paper by Polisetty et al 1 and their recommendations, allied to our previous guidance, will be critical to the future evaluation and use of AI in arthroplasty surgery.…”

Risk factors for unplanned reoperation after corrective surgery for adult spinal deformity

“…9 Therefore, in recognizing and classifying data patterns to measure high-dimensional variables and to anticipate individual surgical outcomes, the non-linear method has a clear advantage. [10][11][12][13] To explain the high accuracy of the black box machine learning predictive model for URO, clinical characteristics and interactions between variables were analyzed through Shapley Additive Explanation (SHAP) values based on the concept of game theory. 14 SHAP values can be used to identify the most important variables in the model, to understand the relationship between the variables and the result, and to diagnose issues with the model's behaviour.…”