A modular fluorescent camera unit for wound imaging

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Wound Healing Research

Background:

• Advanced imaging is crucial for understanding disease and live cell behavior.
• Biophysical markers and real-time analysis are key to monitoring medical conditions.
• Current methods for wound assessment lack real-time, high-resolution biomarker mapping.

Purpose of the Study:

• To develop and apply a novel modular, hand-held fluorescent microscope for real-time biological analysis.
• To map pH and nitric oxide (NO) in subcutaneous wounds to understand healing dynamics.
• To utilize machine learning for analyzing biomarker spatiotemporal trends and predicting healing outcomes.

Main Methods:

• Development of a modular, hand-held fluorescent microscope with sub-millimeter resolution.
• Application of the microscope to map pH and nitric oxide (NO) in subcutaneous wounds.
• Employing machine learning algorithms for clustering biomarker data and identifying healing patterns.

Main Results:

• Spatiotemporal pH mapping revealed a concentric gradient and stabilization at the wound edge.
• Nitric oxide (NO) clustering demonstrated dynamic changes in concentration and size during wound healing.
• Machine learning analysis enabled prediction of wound healing day and re-epithelialization based on biomarker trends.

Conclusions:

• The developed imaging system provides unprecedented insights into wound healing biomarkers.
• Biomarker mapping and machine learning analysis can inform future wound healing studies and treatment strategies.
• This technology paves the way for integrating imaging with bioelectronic devices for closed-loop wound management.