Artificial neural networks trained on simulated multispectral data for real-time imaging of skin microcirculatory blood oxygen saturation.

ABSTRACT:

Significance

Imaging blood oxygen saturation ( SO2 ) in the skin can be of clinical value when studying ischemic tissue. Emerging multispectral snapshot cameras enable real-time imaging but are limited by slow analysis when using inverse Monte Carlo (MC), the gold standard for analyzing multispectral data. Using artificial neural networks (ANNs) facilitates a significantly faster analysis but requires a large amount of high-quality training data from a wide range of tissue types for a precise estimation of SO2 .

Aim

We aim to develop a framework for training ANNs that estimates SO2 in real time from multispectral data with a precision comparable to inverse MC.

Approach

ANNs are trained using synthetic data from a model that includes MC simulations of light propagation

SUBMITTER: Larsson M 

PROVIDER: S-EPMC11234456 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature