Culture-Free Microfluidics for Ultra-Rapid Antimicrobial Susceptibility Testing with AI in Resource-Limited Settings

Area of Science:

• Biomedical Engineering
• Microfluidics
• Diagnostics

Background:

• Antimicrobial resistance (AMR) is a global health crisis, exacerbated by antibiotic misuse.
• Current antibiotic susceptibility testing (AST) methods are slow and require extensive infrastructure, limiting their use in resource-limited settings.
• There is a critical need for rapid, accessible, and reliable diagnostics to combat AMR effectively.

Purpose of the Study:

• To develop a low-cost, self-healing microfluidic chip for rapid, culture-free AST.
• To integrate bacterial enrichment, antibiotic gradient generation, and automated result analysis into a single platform.
• To provide a transformative tool for AST, particularly in resource-limited areas.

Main Methods:

• A centrifugal microfluidic chip with self-healing valves (μCFC-shv) was designed and fabricated for $0.62.
• The chip integrates 1000-fold bacterial enrichment, antibiotic gradient generation, and AST.
• A machine learning model was developed for automated result analysis, mitigating light interference and subjective interpretation.

Main Results:

• The μCFC-shv achieved pathogen detection within 5 minutes post-enrichment with 100% sensitivity and specificity.
• Culture-free AST was completed in 30 minutes of antibiotic exposure with 97.39% accuracy across 306 clinical cases.
• Automated analysis using a machine learning model yielded 98.83% accuracy.

Conclusions:

• The μCFC-shv offers a rapid, cost-effective, and reliable solution for AST, overcoming current technological limitations.
• This platform is particularly suited for resource-limited regions, enabling faster clinical decisions and improved AMR management.
• The integration of microfluidics, self-healing valves, and machine learning represents a significant advancement in diagnostic technology for global health challenges.