Abstract
As we experience a technological revolution unlike any other time in history, spinal surgery as a discipline is poised to undergo a dramatic transformation. As enormous amounts of data become digitized and more readily available, medical professionals approach a critical juncture with respect to how advanced computational techniques may be incorporated into clinical practices. Within neurosurgery, spinal disorders in particular, represent a complex and heterogeneous disease entity that can vary dramatically in its clinical presentation and how it may impact patients’ lives. The spectrum of pathologies is extremely diverse, including many different etiologies such as trauma, oncology, spinal deformity, infection, inflammatory conditions, and degenerative disease among others. The decision to perform spine surgery, especially complex spine surgery, involves several nuances due to the interplay of biomechanical forces, bony composition, neurologic deficits, and the patient's desired goals. Adult spinal deformity as an example is one of the most complex, given its involvement of not only the spine, but rather the entirety of the skeleton in order to appreciate radiographic completeness. With the vast array of variables contributing to spinal disorders, treatment algorithms can vary significantly, and it is very difficult for surgeons to predict how patients will respond to surgery. As such, it will become imperative for spine surgeons to utilize the burgeoning availability of advanced computational tools to process unprecedented amounts of data and provide novel insights into spinal disease. These tools range from predictive models built using machine learning algorithms, to deep learning methods for imaging analysis, to natural language processing that can mine text from electronic medical records or transcribed patient visits – all to better treat the intricacies of spinal disorders. The adoption of such techniques will empower patients and propel spine surgeons into the era of personalized medicine, by allowing clinical plans to be tailored to address individual patients’ needs. This paper, which exists in the context of a larger body of literatutre, provides a comprehensive review of the current state and future of artificial intelligence and machine learning with a particular emphasis on Adult spinal deformity surgery.
Highlights
Introduction to artificial intelligenceApplications for spine surgerySpine surgery as a specialty is rapidly approaching a critical juncture, as medicine embraces a new era of precision medicine driven largely by unprecedented amounts of available data and advanced computational techniques
This study presented a major stride forward in the realm of machine learning and artificial intelligence (AI), and showcased how powerful unsupervised learning techniques could help create new AI-based classification systems to facilitate treatment optimization for surgeons by highlighting treatment patterns predicted to yield optimal improvement with lowest risk
As we embrace the era of genomic medicine, combined with the digitization and widespread availability of enormous amounts of data, advanced computational techniques will become critical to help physicians analyze vast amounts of data that would be impossible without computer assistance
Summary
Introduction to artificial intelligenceApplications for spine surgerySpine surgery as a specialty is rapidly approaching a critical juncture, as medicine embraces a new era of precision medicine driven largely by unprecedented amounts of available data and advanced computational techniques. Combining the massive amounts of available data with powerful new computational methods, we have the ability to harness the power of artificial intelligence (AI). While the broader goal of a generalized and automated intelligence remains beyond our scope, we can use the tools of AI to develop systems that recreate the characteristics of human intelligence − to learn from immense datasets, make decisions, provide recommendations, and adapt to new data/circumstances. Through the use of sophisticated concepts such as artificial neural networks and robust machine learning methods we can develop systems that can dynamically learn from data and use that to inform future behavior and decision making. Machine learning exists as a branch of AI that utilizes computer algorithms to learn from data and prior experiences to build intelligent models. Machine learning algorithms allow the computer to extract patterns inherent within datasets without user-defined or pre-determined rules, to learn relationships from the data and make specific predictions or determinations
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