Gibbs, Wende N.

Bone health is a key determinant of success in spine surgery, making preoperative assessment of bone quality essential to optimal surgical risk stratification. Magnetic resonance imaging (MRI)-based vertebral bone quality (VBQ) score offers a novel approach to assess bone health in spine surgery candidates. The ability of MRI to assess bone quality without exposure to ionizing radiation makes it a potentially advantageous alternative to other traditional measures of bone density. VBQ has additionally shown potential to predict adverse outcomes, such as fragility fractures, instrumentation failure, subsidence and proximal junctional kyphosis. Variations of VBQ, such as endplate bone quality, S1 VBQ, and cervical VBQ, provide targeted insights at specific anatomical regions and potentially enhance the predictive accuracy of VBQ. However, clinical application of VBQ is limited by variability in MRI systems, patient-specific factors, and lack of standardized threshold values. This review aims to critically evaluate VBQ scores as an opportunistic, MRI-based assessment of bone health and its potential role in predicting surgical outcomes. While VBQ may provide some valuable insights into bone health, its role in preoperative risk assessment likely remains supplementary and requires further research to establish clinical validity and optimal cutoffs.

Study Design: Narrative review. Objectives: Artificial intelligence (AI) and machine learning (ML) have emerged as disruptive technologies with the potential to drastically affect clinical decision making in spine surgery. AI can enhance the delivery of spine care in several arenas: (1) preoperative patient workup, patient selection, and outcome prediction; (2) quality and reproducibility of spine research; (3) perioperative surgical assistance and data tracking optimization; and (4) intraoperative surgical performance. The purpose of this narrative review is to concisely assemble, analyze, and discuss current trends and applications of AI and ML in conventional and robotic-assisted spine surgery. Methods: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2019 examining AI, ML, and robotics in spine surgery. Key findings were then compiled and summarized in this review. Results: The majority of the published AI literature in spine surgery has focused on predictive analytics and supervised image recognition for radiographic diagnosis. Several investigators have studied the use of AI/ML in the perioperative setting in small patient cohorts; pivotal trials are still pending. Conclusions: Artificial intelligence has tremendous potential in revolutionizing comprehensive spine care. Evidence-based, predictive analytics can help surgeons improve preoperative patient selection, surgical indications, and individualized postoperative care. Robotic-assisted surgery, while still in early stages of development, has the potential to reduce surgeon fatigue and improve technical precision.