Machine learning assisted hypersperal imaging

Machine learning assisted hypersperal imaging detects and characterizes nanoparticles in ex vivo tissue sections by applying machine learning and adaptive algorithms to hyperspectral dark-field microscopy images to map biodistribution and particle–tissue interactions.

Key Features:

• Adaptive Algorithms: Employs machine learning-based adaptive algorithms to analyze hyperspectral dark-field images for nanoparticle detection and characterization.
• Non-Destructive Analysis: Analyzes large fields of view from hyperspectral microscopy without physically altering ex vivo tissue sections.
• High Sensitivity and Specificity: Detects single nanoparticles within complex biological environments with high sensitivity and specificity.
• Quantitative Identification: Quantitatively identifies and counts particles in ex vivo tissue sections to enable biodistribution profiling.
• Organ-Specific Clearance Patterns: Enables analysis of accumulation patterns influenced by organ-specific clearance, particle size, and molecular specificity of nanoparticle surface coatings.

Scientific Applications:

• Biodistribution Profiling: Characterizes the distribution of nanoparticles used as biomedical imaging probes and potential therapeutic agents.
• Sub-Organ Distribution Studies: Maps sub-organ distribution of nanoparticles, including gold nanoparticles in mice and their localization within tumors.

Methodology:

Capture hyperspectral dark-field microscopy images of ex vivo tissue sections after nanoparticle administration, then apply machine learning-based adaptive algorithms to analyze spectral signatures, detect and characterize nanoparticles, and map their distribution.