Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and...

Area of Science:

• Microbiology
• Computational Biology
• Medical Diagnostics

Background:

• Traditional bacterial diagnostics and antimicrobial susceptibility testing (AST) face limitations in speed, accuracy, and scalability.
• Current methods often require specialized infrastructure and expertise, hindering accessibility, particularly in resource-limited settings.
• Artificial intelligence (AI) presents a promising avenue to overcome these challenges.

Purpose of the Study:

• To review the transformative role of AI in bacterial detection and AST.
• To explore various machine learning and deep learning models applied in AI-driven diagnostics.
• To highlight AI's potential in resource-limited settings and future advancements.

Main Methods:

• Leveraging machine learning models like Random Forest, Support Vector Machines (SVM), and deep learning architectures (CNNs, transformers).
• Examining AI applications across diverse detection techniques including microscopy, spectroscopy (Raman, SERS), mass spectrometry, and sensor-based methods.
• Analyzing AI's role in automating data analysis and enabling real-time results.

Main Results:

• AI significantly enhances the speed, accuracy, and scalability of bacterial diagnostics and AST.
• AI-based approaches offer cost-effective, portable solutions suitable for resource-limited environments.
• AI facilitates remote diagnostics via smartphone integration and telemedicine.

Conclusions:

• AI is revolutionizing bacterial diagnostics and AST, improving efficiency and accessibility.
• AI integration minimizes human error and optimizes healthcare outcomes.
• Future directions point towards further advancements in AI-driven bacterial detection and susceptibility testing.