Work

Decoding elastin–collagen resemblance in keloid scar through label-free imaging and machine learning

Year: 2026

Type: article

Abstract: Significance: Label-free imaging of keloid scar tissues and inter-channel characterization of fibrous structures provide insights for understanding the process of extracellular matrix (ECM) remodeling during human skin aberrant wound healing. Aim: human skin samples, based on endogenous signals of elastin and collagen fibers, and an algorithm is designed to quantify the resemblance in morphology and structure between the two fiber components. Approach: Based on two-photon excitation fluorescence images of elastin fibers and second harmonic generation images of collagen fibers in normal, keloid, and adjacent skin samples, a parameter termed "resemblance metric" (RM) is developed to quantify the morphological and organizational similarity of the two fiber components within the human keloid scar model. The application potential of this method is demonstrated by identifying inter-heterotypic-fibrous resemblance features of three tissue types with high sensitivity. Results: Keloid scar tissues exhibit the highest elastin-collagen resemblance level, and adjacent tissues are the most heterogeneous. Using this parameter, adjacent tissues are identified with an accuracy higher than 98%. Conclusions: The high sensitivity of RM in interpreting the elastin-collagen resemblance within the human keloid scar model reveals a perspective in understanding the mechanism of ECM remodeling.

Source: Journal of Biomedical Optics

DOI: 10.1117/1.jbo.31.3.036005