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A Method to Study the Correlation Between Local Collagen Structure and Mechanical Properties of Atherosclerotic Plaque Fibrous Tissue

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Published on: November 11, 2022

Classification of collagen remodeling in asthma using second-harmonic generation imaging, supervised machine learning

Natasha N Kunchur¹, Joshua J A Poole¹, Jesse Levine¹

¹Department of Systems and Computer Engineering at Carleton University, Ottawa, ON, Canada.

Frontiers in Bioinformatics

|May 2, 2025

Summary

This summary is machine-generated.

Second harmonic generation (SHG) imaging and machine learning accurately characterize airway remodeling in asthma by analyzing fibrillar collagen. This method distinguishes between healthy and asthmatic lung tissue, improving disease understanding.

Keywords:

airway remodeling asthma collagen machine learning second harmonic generation texture analysis

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Area of Science:

Pulmonary Medicine
Biomedical Imaging
Computational Pathology

Background:

Airway remodeling, characterized by increased fibrillar collagen, is prevalent in asthma and impairs lung function.
Conventional histological methods struggle to accurately visualize extracellular matrix (ECM) proteins in their native state.
Second harmonic generation (SHG) offers label-free, high-resolution imaging of fibrillar collagen, crucial for understanding asthma-related lung elasticity loss.

Purpose of the Study:

To develop and validate a computational pipeline for quantifying and characterizing fibrillar collagen in human lung tissue using SHG imaging.
To differentiate between remodeled airways in asthma and control lung tissue based on collagen morphology.
To assess the potential of texture analysis and machine learning in identifying pathological changes in airway ECM.

Main Methods:

SHG imaging was used to visualize fibrillar collagen in lung tissue from 13 human donors (asthmatic and control).
A custom textural classification pipeline extracted 80 features from collagen images using matrices like GLCM, GLSZM, GLRLM, GLDM, and NGTDM.
Feature selection methods refined the dataset, and a support vector machine (SVM) model was trained for classification.

Main Results:

The pipeline successfully quantified and characterized collagen distribution in remodeled versus control airways.
A refined subset of textural features achieved high predictive importance.
The SVM model demonstrated strong performance, with an AUC-ROC of 94% ± 0.0001 in classifying remodeled airway collagen.

Conclusions:

SHG imaging combined with texture analysis and supervised machine learning can accurately characterize morphological variations in airway collagen.
This approach provides a powerful tool for objective assessment of airway remodeling in asthma.
The findings highlight the utility of label-free imaging and computational methods in advancing the understanding of fibrotic lung diseases.