Effects of various cross-linked collagen scaffolds on wound healing in rats model by deep-learning CNN.

Abstract

Scar tissue is connective tissue formed on the wound during the wound-healing process. The most significant distinction between scar tissue and normal tissue is the appearance of covalent cross-linking and the amount of collagen fibers in the tissue. This study investigates the efficacy of four types of collagen scaffolds in promoting wound healing and regeneration in a Sprague-Dawley murine model-the histomorphology analysis of collagen scaffolds and developing a deep learning model for accurate tissue classification. Four female rats (n = 24) groups received collagen scaffolds prepared through physical and chemical crosslinking. Wound healing progress was evaluated by monitoring granulation tissue formation, collagen matrix organization, and collagen fiber deposition, with histological scoring for quantification-the EDC and HA groups demonstrated enhanced tissue regeneration. The EDC and HA groups observed significant differences in wound regeneration outcomes. Deep-learning CNN models with data augmentation techniques were used for image analysis to enhance objectivity. The CNN architecture featured pre-trained VGG16 layers and global average pooling (GAP) layers. Feature visualization using Grad-CAM heatmaps provided insights into the neural network's focus on specific wound features. The model's AUC score of 0.982 attests to its precision. In summary, collagen scaffolds can promote wound healing in mice, and the deep learning image analysis method we proposed may be a new method for wound healing assessment.