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Advancing Biosensors with Machine Learning.

Feiyun Cui¹, Yun Yue², Yi Zhang³

¹Department of Chemical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, Massachusetts 01609, United States.

|November 13, 2020

Summary

This summary is machine-generated.

Machine learning (ML) enhances biosensor capabilities for detection and diagnosis. This review explores advanced ML methods, including deep learning, to improve biosensor performance and data analysis.

Keywords:

CNN SERS artificial intelligence (AI)chemometrics deep learning intelligent biosensor machine learning (ML)multidimensional features sensing data wearable electronics

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Area of Science:

Biomedical Engineering
Data Science
Analytical Chemistry

Background:

Chemometrics are vital for biosensor applications in detection, analysis, and diagnosis.
Machine learning (ML), a subset of artificial intelligence (AI), has seen significant advancements.
Advanced ML methods, particularly deep learning, are underutilized in the biosensor field.

Purpose of the Study:

To systematically review the benefits of ML in biosensor technology.
To summarize the pros and cons of common ML algorithms for biosensor data.
To highlight the potential of deep learning (CNN, RNN) for biosensor development.

Main Methods:

Discussion of ML algorithms applied to biosensor data analysis.
Review of diverse biosensor types enhanced by ML (electrochemical, wearable, spectral, fluorescence, colorimetric).
Introduction to ML in biosensor networks and data fusion.

Main Results:

ML offers significant advantages for biosensor data analysis and interpretation.
Deep learning methods like CNN and RNN show particular promise for complex biosensor data.
ML integration expands the scope of biosensor applications in detection, analysis, and diagnosis.

Conclusions:

ML, especially deep learning, can substantially advance biosensor performance.
This review bridges the gap between ML and biosensors, expanding chemometric possibilities.
The integration of ML promises improved detection, analysis, and diagnostic capabilities through biosensors.