Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review

• Published in: Curēus (Palo Alto, CA) Vol. 16; no. 5; p. e61400
• Main Authors: Mulpuri, Rajendra P, Konda, Nikhitha, Gadde, Sai T, Amalakanti, Sridhar, Valiveti, Sindhu Chowdary
• Format: Journal Article
• Language: English
• Published: United States Springer Nature B.V 30.05.2024; Cureus
• Subjects: Artificial intelligence; Automation; Boolean; Case reports; Deep learning; Healthcare Technology; Keywords; Machine learning; Neural networks; Neurology; Neurosurgery; Robotics; Subject heading schemes; Systematic review; machine learning; neuroregeneration; regenerative medicine; artificial intelligence; deep neural networks; neural networks
• ISSN: 2168-8184; 2168-8184
• DOI: 10.7759/cureus.61400

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.