Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review.

Autor:	Mulpuri RP; General Medicine, All India Institute of Medical Sciences, Mangalagiri, IND., Konda N; Internal Medicine, Alluri Sitarama Raju Academy of Medical Sciences, Eluru, IND., Gadde ST; General Medicine, All India Institute of Medical Sciences, Mangalagiri, IND., Amalakanti S; General Medicine, All India Institute of Medical Sciences, Mangalagiri, IND., Valiveti SC; Intern General Medicine, Sri Padmavathi Medical College for Women, Tirupati, IND.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2024 May 30; Vol. 16 (5), pp. e61400. Date of Electronic Publication: 2024 May 30 (Print Publication: 2024).
DOI:	10.7759/cureus.61400
Abstrakt:	Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing on AI/ML in neuroregeneration were selected from a total of 247. Two researchers independently conducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT) 2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals included diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronic health records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard ML algorithms constituted 29%. The review underscores the growing interest in AI/ML for neuroregenerative medicine, with increasing publications. These technologies aid in diagnosing diseases and facilitating functional recovery through robotics and targeted stimulation. AI-driven drug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressing existing limitations remains crucial in this rapidly evolving field. Competing Interests: Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work. (Copyright © 2024, Mulpuri et al.)
Databáze:	MEDLINE
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