BACKGROUND: Timely palliative care (PC) consultations offer demonstrable benefits for patients with traumatic brain injury (TBI), yet their implementation remains inconsistent. This study employs machine learning methods to identify distinct patient ...
BACKGROUND AND OBJECTIVE: Preoperative neurosurgical planning is an important step in avoiding surgical complications, reducing morbidity, and improving patient safety. The incursion of machine learning (ML) in this domain has recently gained attenti...
PURPOSE: Vestibular schwannomas (VSs) represent the most common cerebellopontine angle tumors, posing a challenge in preserving facial nerve (FN) function during surgery. We employed the Extreme Gradient Boosting machine learning classifier to predic...
OBJECTIVE: Deep learning enables precise hand tracking without the need for physical sensors, allowing for unsupervised quantitative evaluation of surgical motion and tasks. We quantitatively assessed the hand motions of experienced cerebrovascular n...
Artificial intelligence (AI) has evolved from science fiction to a technology infiltrating everyday life. In neurosurgery, clinicians and researchers are exploring ways to implement this powerful tool to improve the safety and efficiency of the perio...
Artificial Intelligence (AI) is revolutionizing neurosurgery by enhancing diagnostic accuracy, surgical planning, and personalized patient care. Despite challenges like data privacy and bias, AI's integration promises to transform surgical outcomes a...
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