The objective of this review is to evaluate the current literature on four key technological domains (AI/ML, 3D mapping, Neuroprosthetics, and BCIs) and their potential to transform neurosurgery into a precision-based, personalized field. The aim is to also elaborate on the specific hurdles that prevent the widespread integration of these advanced technologies into routine clinical practice.
The ongoing advancements of neurosurgery are driven by rapid technological advancements aimed at enhancing precision and patient-specific outcomes. Despite this neurosurgery faces persistent challenges due to the complex CNS anatomy and the need for micron-level precision. Recent innovations including artificial intelligence (AI), machine learning (ML), three-dimensional (3D) brain mapping, brain-computer interfaces (BCIs) and neuro-prosthetic technologies are transforming neurosurgical practice into a data-driven, precision-based discipline.
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Role of AI/ML in neurosurgery is important in the diagnostic, predictive and operative domains. Predictive models have shown a performance, with accuracies often measured by AUC, greater than 0.7, reaching as high as 97.5% accuracy. Diagnostic applications also demonstrate high reliability, with sensitivity and specificity values exceeding 90% in identifying complex conditions such as spinal cord compression and intracranial aneurysm. Furthermore, 3D printing has proven effective in enhancing neurosurgical precision, achieving a high accuracy rate for screw placement in neurovascular surgery. Neuro-prosthetics and Brain-Computer Interfaces (BCIs) have also achieved major milestones including adaptive deep brain stimulation, restoration of cortical motor control, and epidural electrical stimulation for paraplegia significantly facilitating the recovery of sensory and motor function. The integration of Augmented and Virtual Reality further enhances intraoperative visualization and surgical navigation.