Postoperative care of neurosurgical patients: general principles

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.

Perioperative Care of the Patient With Acoustic Neuroma

Acoustic neuromas, also known as vestibular schwannomas, are slow-growing, benign tumors that develop on the eighth cranial nerve. Common signs and symptoms of an acoustic neuroma include hearing loss and balance disturbances. A physical examination, a hearing evaluation, and diagnostic imaging assist in the diagnosis of an acoustic neuroma. Patients with a confirmed tumor have three treatment options: observation, stereotactic radiosurgery, and surgical removal. Complications include cerebrospinal fluid leakage, damage to ancillary brain structures, facial nerve damage, and bleeding or vascular injury. This article focuses on the surgical removal of an acoustic neuroma and the role of the perioperative nurse in the perioperative care of the patient.