Integrating AI with Biosensors and Voltammetry for Neurotransmitter Detection and Quantification: A Systematic Review

Area of Science:

• Neuroscience
• Biomedical Engineering
• Artificial Intelligence

Background:

• Accurate diagnosis of neurodegenerative diseases relies on real-time monitoring of neurotransmitter (NT) dynamics in complex biological fluids.
• Current biosensors lack sensitivity and selectivity, hindering reliable NT detection and quantification.
• Challenges include signal convolution, electrode fouling, and inter-NT crosstalk, limiting diagnostic accuracy.

Purpose of the Study:

• To review and synthesize research on artificial intelligence (AI) applications for automated neurotransmitter detection and quantification.
• To evaluate machine learning (ML), pattern recognition (PR), and deep learning (DL) for improving NT estimation in complex biological fluids.
• To explore AI's potential in overcoming limitations of traditional biosensors for neurodegenerative disease diagnostics.

Main Methods:

• Systematic review of 33 peer-reviewed studies integrating AI in neurotransmitter estimation.
• Analysis of commonly studied NTs, detection methodologies, and data acquisition techniques.
• Categorization of AI algorithms applied for signal processing and interpretation of NT data.

Main Results:

• AI-based approaches show significant potential in deconvoluting complex, multiplexed NT signals.
• AI enables more accurate real-time NT estimation, overcoming traditional biosensor limitations.
• Reviewed AI methodologies are categorized by application and performance in NT signal analysis.

Conclusions:

• AI-enhanced NT monitoring is a promising avenue for advancing neurodegenerative disease diagnostics and therapeutics.
• AI integration offers potential for more effective and personalized treatments, including closed-loop deep brain stimulation (CLDBS).
• Further research is needed to address challenges like sensor stability and NT interaction complexity.