Artificial Intelligence and Robotics in Spine Surgery

Abstract

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.

Intelligence-Based Spine Care Model: A New Era of Research and Clinical Decision-Making

“…Artificial intelligence is already being harnessed for surgical planning and image processing, both of which are critical to robotic surgery. 84 At its core, robotic systems consist of three major components: sensors, end effectors, and a control matrix that synthesizes applied data into action. 85 Machine learning has the potential to augment the interaction between the robot, its environment, and the surgeon, whereby the ML augmentation is used to continuously analyze the data generated by the environment so that the robotic system learns from its own experience.…”

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

“…While this field remains embryonic, AI-robotics have numerous proposed advantages over existing surgical practise, such as resistance to fatigue, reduction in tremors, and increased precision [ 6 ]. In recent decades, a range of neurosurgical robotics have been introduced—the individual analysis of which are beyond the scope of this review [ 6 , 15 , 125 , 126 , 127 , 128 , 129 , 130 ]. Much excitement and promise has been generated around the Da Vinci surgical robot.…”

“…It is feasible that AI be used to further research and even generate national guidance for certain tumours. In their paper regarding AI in spinal surgery, Rasouli et al comment on how AI may surpass the current means of guideline generation, which are dependent on the interpretation of large amounts of data combined with clinical expertise by expert panels [ 127 ]. Rasouli et al highlight that the guidance generated is influenced by the quality of data that are presented to the panels, as well as the ability for the panels to accurately pick up on all of the salient points [ 127 , 211 ].…”

AI MSK clinical applications: spine imaging

“…AI techniques are already being used both in research settings and for commercial purposes for different tasks of routine workflows in spine imaging. Similar to approaches for categorizing AI-suitable tasks in spine surgery [9], the impact of AI may also be categorized into different spheres of action for (spine) radiology.…”

“…AI as an umbrella term has heavily influenced our daily routine inside and outside of our radiologic work and has the potential to impact MSK imaging routine in a disruptive way. The scale of the disruption may be comparable to, or even greater than, other changes of the past such as the widespread availability of computed tomography (CT), magnetic resonance imaging (MRI), and picture archiving and communication systems (PACS) [9]. However, while new AI technologies have significant potential, they require basic education of the operator, and more importantly, they present novel challenges and risks that must be kept in mind while pushing forward towards new frontiers.…”