Presentation
2 March 2022 Quantifying pharmacodynamics in a mouse inflammation model via CARS and SRS imaging paired with machine learning analysis
Author Affiliations +
Abstract
The onset and progression of dermal inflammation are easy to diagnose, but challenging to quantify, stage, and measure. CARS and SRS imaging are modalities capable of providing insight into the dynamics of structural and drug/fluid concentration changes throughout a time course of tissue imaging. This work displays two main avenues of exploration using dermatitis-induced mouse models. First, we track inflammation development and resolution over a four-day time course, capturing CARS and SRS image data at multiple time points to use in a machine learning (ML) based approach trained to classify the extent of inflammation in the provided images. Second, we treat mice with anti-inflammatory agents to determine whether these agents truly help with inflammation resolution, using our ML-based approach trained on structural and concentration rich images as a proxy for the pharmacodynamic response. We additionally use ML interpretability methods to aid in the justification of our results.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daniel Greenfield and Conor L. Evans "Quantifying pharmacodynamics in a mouse inflammation model via CARS and SRS imaging paired with machine learning analysis", Proc. SPIE PC11973, Advanced Chemical Microscopy for Life Science and Translational Medicine 2022, PC1197308 (2 March 2022); https://doi.org/10.1117/12.2608687
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KEYWORDS
Ear

Inflammation

CARS tomography

Machine learning

Convolutional neural networks

Diagnostics

Mouse models

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