Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning.

ABSTRACT:

Significance

Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions.

Aim

Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning.

Approach

Multiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning mode

SUBMITTER: Meng J 

PROVIDER: S-EPMC10082605 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature