RESUMO
Intraoperative navigation is critical during spine surgery to ensure accurate instrumentation placement. From the early era of fluoroscopy to the current advancement in robotics, spinal navigation has continued to evolve. By understanding the variations in system protocols and their respective usage in the operating room, the surgeon can use and maximize the potential of various image guidance options more effectively. At the same time, maintaining navigation accuracy throughout the procedure is of the utmost importance, which can be confirmed intraoperatively by using an internal fiducial marker, as demonstrated herein. This technology can reduce the need for revision surgeries, minimize postoperative complications, and enhance the overall efficiency of operating rooms.
RESUMO
In this study, a small sample of patients' neuromonitoring data was analyzed using machine learning (ML) tools to provide proof of concept for quantifying complex signals. Intraoperative neurophysiological monitoring (IONM) is a valuable asset for monitoring the neurological status of a patient during spine surgery. Notably, this technology, when operated by neurophysiologists and surgeons familiar with proper alarm criteria, is capable of detecting neurological deficits. However, non-surgical factors, such as volatile anesthetics like sevoflurane, can negatively influence robust IONM signal generation. While sevoflurane has been shown to affect the latency and amplitude of somatosensory evoked potential (SSEP), a more complex and nuanced analysis of the SSEP waveform has not been performed. In this study, signal processing and machine learning techniques were used to more intricately characterize and predict SSEP waveform changes as a function of varying end-tidal sevoflurane concentration. With data from ten patients who underwent spinal procedures, features describing the SSEP waveforms were generated using principal component analysis (PCA), phase space curves (PSC), and time-frequency analysis (TFA). A minimum redundancy maximum relevance (MRMR) feature selection technique was then used to identify the most important SSEP features associated with changing sevoflurane concentrations. Once the features carrying the maximum amount of information about the majority of signal waveform variability were identified, ML models were used to predict future changes in SSEP waveforms. Linear regression, regression trees, support vector machines, and neural network ML models were then selected for testing. Using SSEP data from eight patients, the models were trained using a range of features selected during MRMR calculations. During the training phase of model development, the highest performing models were identified as support vector machines and regression trees. After identifying the highest performing models for each nerve group, we tested these models using the remaining two patients' data. We compared the models' performance metrics using the root mean square error values (RMSEs). The feasibility of the methodology described provides a general framework for the applications of machine learning strategies to further delineate the effects of surgical and non-surgical factors affecting IONM signals.
RESUMO
Intraoperative neuromonitoring (IONM) has become an indispensable surgical adjunct in cervical spine procedures to minimize surgical complications. Understanding the historical development of IONM, indications for use, associated pitfalls, and recent developments will allow the surgeon to better utilize this important technology. While IONM has shown great promise in procedures for cervical deformity, intradural tumors, or myelopathy, routine use in all cervical spine cases with moderate pathology remains controversial. Pitfalls that need to be addressed include human error, a lack of efficient communication, variable alarm warning criteria, and a non-standardized checklist protocol. As the techniques associated with IONM technology become more robust moving forward, IONM emerges as a crucial solution to updating patient safety protocols.
