Advancing collagen‐related pathology assessment through second‐harmonic generation imaging

Abstract Collagen remodelling and dysregulation are the hallmarks of diverse pathological conditions. In this context, second‐harmonic generation (SHG) imaging has emerged as a powerful label‐free modality for assessing collagen. This offers submicron resolution, intrinsic optical sectioning, and deeper imaging capabilities without the need for exogenous agents. Additionally, polarisation‐resolved SHG (P‐SHG) further enhances orientation‐sensitive information on collagen architecture. This enables the elucidation of subtle structural alterations that are often invisible to conventional imaging techniques. This nonlinear technique has demonstrated utility in diagnosing diseases, particularly in assessing tumour progression, where collagen remodelling is correlated with disease severity and prognosis. Furthermore, quantitative image analysis, using metrics such as fibre density, orientation, anisotropy, and alignment, provides objective measures of collagen remodelling. P‐SHG complements these analyses by enabling the retrieval of molecular susceptibility ratios (e.g., χ33/χ31), degrees of polarisation (DOLP), and orientation distribution functions, yielding more profound insights into the fibrillar ultrastructure and molecular organisation. These quantitative descriptors are being increasingly integrated into clinically relevant feature extraction methods. The incorporation of these features in artificial intelligence (AI) methods has enhanced SHG‐based pathological assessment. AI‐driven models can automatically classify tissues, detect pathological patterns, and correlate the collagen microstructure with clinical outcomes, addressing the bottleneck of manual interpretation and enhancing reproducibility. Future perspectives highlight the integration of SHG and P‐SHG with deep‐tissue multiphoton imaging, accompanied by explainable AI/ML‐driven quantification, which will improve diagnostic precision, reproducibility, and clinical adoption. Ultimately, the convergence of optical innovation, deep learning, and translational engineering will lead to the establishment of collagen pathology assessment through SHG and P‐SHG as reliable, accessible tools for diverse clinical applications.