Neural decoding with artificial intelligence for personalized robotic neurosurgery
1 Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
2 Department of AI and Data Analytics, University of Bradford, Bradford, UK.
3 Department of Software Engineering, University of Hertfordshire, Hatfield, UK.
Review
Open Access Research Journal of Science and Technology, 2023, 07(02), 040-048.
Article DOI: 10.53022/oarjst.2023.7.2.0015
Publication history:
Received on 22 January 2023; revised on 20 March 2023; accepted on 24 March 2023
Abstract:
Robotic neurosurgery has significantly advanced surgical precision and outcomes, with personalization becoming crucial in optimizing treatment for individual patients. The crucial aspect of personalization has become increasingly apparent in optimizing treatment for individual patients, allowing for tailored and precise interventions that can significantly improve patient care and recovery. This literature review explores the integration of neural decoding and artificial intelligence (AI) in personalized robotic neurosurgery, focusing on current techniques, applications, and future directions. Neural decoding, which interprets neural signals to guide surgical interventions, is examined alongside its limitations, including spatial resolution and invasiveness. The review highlights how AI and machine learning enhance neural decoding by improving pattern recognition and predictive capabilities, thus enabling real-time adaptations during surgery. Personalized robotic neurosurgery leverages advanced imaging and real-time data to tailor surgical approaches, improving precision and reducing complications. The integration of neural decoding and AI into robotic systems presents significant benefits, such as enhanced accuracy and personalized care, but also faces challenges related to technology integration, cost, and reliability. Future research should address these challenges by developing robust algorithms and expanding clinical applications. This review provides a comprehensive overview of how combining these technologies can advance personalized neurosurgical practices and improve patient outcomes.
Keywords:
Robotic neurosurgery; Personalization; Neural decoding; Artificial intelligence; Machine learning
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0