**5. Intraoperative applications**

The intraoperative phase of patient care revolves around optimizing the neurosurgeon's functionality and performance in the operating room (OR). AI's role intraoperatively includes augmented reality (AR), ML for pathology and neurooncologic applications, using algorithms to automate identification of intraoperative injuries based on the operative note.

Augmented reality has a myriad of intraoperative uses in both cranial and spinal procedures. From the cerebrovascular standpoint, AR has been used to decrease the craniotomy size and delineate aneurysm architecture for safer aneurysm clipping [138]. AR has also been used to superimpose white matter tracts onto the surgical field as well as identify eloquent brain regions during tumor resections [139]. The implementation of AR was shown to result in significantly greater rates of total resection with better preservation of critical functions such as vision, speech, and motor [139]. Head-up AR microscope displays with navigation were found to be more accurate than traditional microscopy with navigation based on fiducial or automatic intraoperative CT registration in the setting of transsphenoidal surgeries [140]. Rychen et al. described the successful use of AR to fuse CTA, DSA, and TOF MRI imaging with neuronavigation for superficial temporal artery to middle cerebral artery (STA-MCA) bypass operations [141]. Perhaps one of the most impressive features of these applications is that augmented reality is formulated to work with current microscopes and neuronavigation systems that are commonly used for neurosurgical procedures, rather than requiring an entirely new device.

Resection margins are of the utmost importance in the resection of malignant tumors as remnants of malignant tissue led to the recurrence of disease and decreased survival. Real time analysis of resection margins typically requires an experienced neuropathologist, as well as a processor well versed in chemistry [142]. ML was employed to process samples through the High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) methodology, with high accuracy (median AUC of 85.6% and AUPR of 93.4%) [142]. Jabarkheel et al. established the use of Raman spectroscopy to accurately differentiate benign and malignant tissue intraoperatively in pediatric tumor resections [143].

Spinal procedures also utilize AR to aid in the precise placement of pedicle screws, superimposing trajectories into the surgical field [144]. Computer-assisted navigation (CAN) has a wide range of uses from tumor resection to deformity correction. When utilized for screw placement, CAN reduces the need for fluoroscopic guidance thus decreasing radiation exposure. CAN also increases operative efficiency, which diminishes the operative time and patient exposure to anesthesia [145].

Another promising AI application in spinal surgery is robotics. The SpineAssist (MAZOR Robotics Inc., Caesarea, Israel), ROSA (Medtech, SA, Montpellier, France), the Excelsius GPS Robot (Globus Medical, Inc., Audubon, PA), and the Da Vinci Surgical System (Intuitive Surgical, Sunnyvale, CA) are the four most studied robotic systems available [145]. Each has its strengths and weaknesses, and it is worth
