Integrating machine learning and biosensors in microfluidic devices: A review

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Computer Science

Background:

• Microfluidic devices are crucial for controlled microenvironments in diverse applications.
• Biosensors offer high accuracy and selectivity but require sophisticated data analysis.
• Machine learning (ML) algorithms excel at interpreting complex biosensor signals.

Purpose of the Study:

• To review the advantages of integrating microfluidics, biosensors, and machine learning.
• To highlight the benefits of dual interactions between these technologies.
• To showcase applications of the microfluidics-biosensors-machine learning triad.

Main Methods:

• Literature review of microfluidics, biosensors, and machine learning.
• Analysis of synergistic effects and dual interactions.
• Exploration of existing applications utilizing the triad paradigm.

Main Results:

• The integration of microfluidics, biosensors, and ML creates "intelligent microfluidics".
• ML significantly improves biosensor data interpretation and performance.
• Synergistic interactions offer novel solutions in chemical research, drug development, and clinical diagnostics.

Conclusions:

• The triad of microfluidics, biosensors, and machine learning presents a promising, albeit underutilized, research paradigm.
• This integration enhances analytical capabilities and opens new avenues for point-of-care devices and personalized medicine.
• Further research into this interdisciplinary approach is warranted to fully realize its potential.