Microparticle-Based Biochemical Sensing Using Optical Coherence Tomography and Deep Learning.

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Personalized Medicine

Background:

• Current health monitoring primarily focuses on vital signs, limiting early disease detection.
• Advancing to biochemical monitoring is crucial for personalized medicine and proactive healthcare.
• Existing methods for continuous biochemical analysis often lack non-invasiveness or real-time capabilities.

Purpose of the Study:

• To develop a novel biosensing platform for remote, continuous biochemical monitoring.
• To integrate microparticle-based biosensors with optical coherence tomography (OCT) for enhanced detection.
• To demonstrate the potential for real-time in vivo biochemical analysis.

Main Methods:

• Designed stimuli-responsive polymeric microparticles as dispersible biorecognition units.
• Utilized optical coherence tomography (OCT) to detect submicron volumetric changes in microparticles.
• Modeled microparticles as optical cavities to achieve measurements below OCT's resolution limit.
• Employed deep learning (3D convolutional neural networks) for automated data processing.

Main Results:

• Successfully demonstrated 3D spatiotemporal monitoring of glucose-responsive microparticles in a tissue mimic.
• Showcased real-time tracking of microparticle volumetric changes in response to fluctuating glucose levels.
• Developed an automated pipeline for processing continuous 3D time-series data using deep learning.

Conclusions:

• The developed platform enables remote, continuous biochemical monitoring beyond traditional vital signs.
• This technology holds significant potential for revolutionizing personalized medicine through real-time in vivo biochemical analysis.
• The integration of microparticle biosensors, OCT, and deep learning offers a robust solution for future health monitoring systems.