Advancement of machine learning algorithms in biosensors.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence
• Diagnostics

Background:

• Biosensors are vital for rapid, accurate disease detection and health monitoring.
• Current biosensors face limitations in multi-analyte detection, non-invasive sensing, and ultra-low concentration analysis.
• Machine learning (ML) offers potential solutions to enhance biosensor performance and overcome existing challenges.

Purpose of the Study:

• To review recent advancements in ML-enhanced biosensors for healthcare applications.
• To explore how ML algorithms improve data processing and insight extraction in biosensor technology.
• To identify challenges and future prospects of ML in biosensor development.

Main Methods:

• Review of literature on ML-enhanced biosensors, covering various types (electrochemical, optical, microfluidic, wearable).
• Examination of ML algorithm roles (supervised, unsupervised, deep learning) in improving biosensor performance.
• Analysis of applications in health monitoring, disease diagnosis, and treatment evaluation.

Main Results:

• ML algorithms significantly enhance biosensor capabilities, including classification, regression, clustering, and feature extraction.
• ML integration addresses limitations in accuracy and sensitivity for complex diagnostic scenarios.
• Various biosensor types are effectively augmented by ML for physiological signal analysis.

Conclusions:

• ML-reinforced biosensors hold transformative potential for revolutionizing diagnostics and personalized medicine.
• Key challenges include data privacy, ethical concerns, real-time processing, and fabrication.
• Future directions involve multifunctional biosensors, adaptive learning, and explainable AI for reliable healthcare deployment.