Neurosurgery and artificial intelligence

Abstract

<abstract> <p>Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. The last decade has seen a significant increase in the importance of artificial intelligence (AI) in neurosurgery. AI can provide a great promise in neurosurgery by complementing neurosurgeons' skills to provide the best possible interventional and noninterventional care for patients by enhancing diagnostic and prognostic outcomes in clinical treatment and help neurosurgeons with decision making during surgical interventions to improve patient outcomes. Furthermore, AI is playing a pivotal role in the production, processing and storage of clinical and experimental data. AI usage in neurosurgery can also reduce the costs associated with surgical care and provide high-quality healthcare to a broader population. Additionally, AI and neurosurgery can build a symbiotic relationship where AI helps to push the boundaries of neurosurgery, and neurosurgery can help AI to develop better and more robust algorithms. This review explores the role of AI in interventional and noninterventional aspects of neurosurgery during pre-, intra- and postoperative care, such as diagnosis, clinical decision making, surgical operation, prognosis, data acquisition, and research within the neurosurgical arena.</p> </abstract>

The future of artificial intelligence in neurosurgery: A narrative review

“…The patient outcomes in the case of neurosurgical diseases are based also on several other factors from the age of the patients, ethnicity, economic background, and national and international guidelines. [ 27 ] Quarter of the errors occurring in neurosurgery is the result of technical errors alone, so by intermingling the use of machine learning (ML), we can somewhat work on these errors shortly. [ 27 , 38 ] It has been estimated that preventable medical errors alone had led to the deaths of 98,000 Americans annually, with the surgical errors causing a major cost to the US economy while the ratio of neurosurgical errors is not known which makes the cost-effectiveness of the procedure opaque.…”

“…[ 42 ] In the past decade alone, there has been a great interest shown in the use of AI in neurosurgery. [ 27 ] Modern diagnostic techniques produce large amounts of data that can be interpreted grossly by trained specialists and consultants but the quantitative analysis does require AI and ML as they can provide better results and patterns than observed by humans. The implications of AI in neurosurgical care are in the initial levels and their incorporation into daily clinical practice is yet to be established by working on limitations like the accessibility to high-quality data.…”

“…[ 32 ] The use of advanced technology in the field of neurosurgery has always been on the rise, from using MRI and CT as diagnostic imaging modalities since they were first introduced, neurosurgeons all over the world are using cutting-edge technology for diagnosing and managing patients and to improve patient outcomes. [ 27 ]…”

Augmented and Virtual Reality Usage in Awake Craniotomy: A Systematic Review

“…AR and VR can help with complex neurosurgical procedures in training neurosurgeons, planning surgeries, and helping with patient recovery following the surgery. AR and VR in neurosurgery have been used in multiple neurosurgical sub-specialties, and have aided preoperative training and planning, surgical decision-making, intraoperative work ow, risk minimisation, postoperative clinical assessments, and rehabilitation [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Commentary: Validation of Machine Learning-Based Automated Surgical Instrument Annotation Using Publicly Available Intraoperative Video

“…Neurosurgery, in the past decade, has seen a significant increase in the utilization and applications of machine learning (ML) and artificial intelligence (AI). 1 From enhancement of diagnostic evaluation and prognostic outcomes to complementation of clinical decision making and surgical skills, machine learning has opened new avenues of research across a multitude of disciplines. 2 Enabling automated diagnosis of ultrasound images to even analyzing histological images of tissues, machine learning approaches are allowing advanced medical image analysis that can augment a physician's workflow.…”