Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review

Area of Science:

• Neuroscience and neurosurgery
• Regenerative medicine

Background:

• Artificial intelligence (AI) and machine learning (ML) show significant promise in diverse medical fields.
• Advancements in AI/ML are enhancing brain-computer interfaces, neuroprosthetics, and surgical planning in neuroscience.
• AI/ML applications are poised to revolutionize neuroregeneration by elucidating nervous system complexities, though research is currently fragmented.

Purpose of the Study:

• To conduct a comprehensive review of AI/ML applications in neuroregeneration.
• To identify and assess the quality of existing research in this rapidly evolving field.
• To synthesize findings on AI/ML's role in diagnosing neurological disorders, robotic rehabilitation, and drug discovery for neuroregeneration.

Main Methods:

• Systematic literature search adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
• Selection of 19 English-language papers from 247 identified studies.
• Independent data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT 2018), classifying studies as high, moderate, or low quality.

Main Results:

• The review identified key applications including diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each).
• Common methodologies involved analyzing imaging data (24%), utilizing animal models (24%), and examining electronic health records (12%).
• Deep learning techniques (41%) were more prevalent than standard ML algorithms (29%), indicating a trend towards complex AI models.

Conclusions:

• There is a growing body of research on AI/ML in neuroregenerative medicine, with increasing publication rates.
• AI/ML technologies are instrumental in improving disease diagnosis and facilitating functional recovery through methods like robotics and targeted stimulation.
• AI-driven drug discovery presents a promising avenue for identifying novel neuroregenerative therapies, though current limitations must be addressed.