Advancing collagen-related pathology assessment through second-harmonic generation imaging.

Area of Science:

• Biophotonics and Medical Imaging
• Nonlinear Optics
• Biomolecular Imaging

Background:

• Collagen remodelling is a key indicator in many diseases.
• Conventional imaging struggles to detect subtle collagen alterations.
• Label-free imaging techniques are needed for accurate collagen assessment.

Purpose of the Study:

• To highlight the utility of second-harmonic generation (SHG) and polarization-resolved SHG (P-SHG) for collagen assessment.
• To demonstrate the integration of quantitative image analysis and artificial intelligence (AI) with SHG/P-SHG.
• To discuss the potential of these advanced imaging techniques in clinical diagnostics.

Main Methods:

• Utilizing label-free second-harmonic generation (SHG) imaging for submicron resolution collagen visualization.
• Employing polarization-resolved SHG (P-SHG) to gain orientation-sensitive information on collagen architecture.
• Applying quantitative image analysis metrics (e.g., fibre density, orientation) and AI for pathological assessment.

Main Results:

• SHG and P-SHG provide detailed insights into collagen ultrastructure and molecular organization.
• Quantitative descriptors derived from SHG/P-SHG correlate with disease severity and prognosis.
• AI-driven models enhance automated tissue classification and pathological pattern detection using SHG features.

Conclusions:

• SHG and P-SHG are powerful tools for label-free collagen assessment in pathology.
• The integration with AI significantly improves diagnostic accuracy and reproducibility.
• Future advancements promise wider clinical adoption for diverse applications.