Insight into the potential of algorithms using AI technology as in vitro diagnostics utilizing microbial...

Area of Science:

• Microbiology
• Biotechnology
• Artificial Intelligence

Background:

• The microbiome's role in health is increasingly recognized, yet microbial effector mechanisms remain unclear.
• Microbial extracellular vesicles (MEVs) are key mediators of host-microbe interactions, carrying vital information.
• AI is revolutionizing medicine, offering new diagnostic and therapeutic avenues.

Purpose of the Study:

• To review the integration of MEVs and AI for developing algorithm-based in vitro diagnostics (IVDs).
• To explore the potential of MEV-AI diagnostic tools for personalized medicine.
• To identify key factors and limitations in applying MEV-AI diagnostics.

Main Methods:

• Review of existing literature on MEV-based algorithms and AI technologies.
• Analysis of MEV diagnostic models using various human sample types.
• Evaluation of factors influencing diagnostic accuracy, including sample optimization, AI power, and additional markers.

Main Results:

• MEV-based diagnostic models demonstrate high clinical performance.
• Optimizing sample types and employing powerful AI significantly improve diagnostic accuracy.
• Incorporating additional biomarkers further enhances the diagnostic capabilities of MEV-AI systems.

Conclusions:

• The combination of MEVs and AI presents a powerful approach for personalized medicine diagnostics.
• Standardization of methods, sample size, and analysis techniques are critical for in situ application.
• Future research on MEVs will advance disease understanding and precision medicine strategies.