Abstract
Background:
Keloids are pathological scars commonly arising after skin trauma, mediated by various pathological mechanisms. They exhibit dynamic changes, high internal heterogeneity, and biological characteristics similar to malignant tumors, with high recurrence rates and prolonged treatment cycles. Blood perfusion maps based on Laser Speckle Contrast Imaging (LSCI) can effectively assess the blood perfusion and growth status of keloids.
However, conventional LSCI-based blood flow measurement processes are time-consuming and labor-intensive. Therefore, this study aims to develop a deep learning-based automated segmentation and blood perfusion prediction system for keloids, enabling remote, efficient, and objective evaluation of their blood perfusion and growth status.