Multi-functional scar tissue discrimination platform construction and exploration of molecular mechanism for scar formation

Scars that form after skin injury can cause structural and functional skin damage. Currently, scar tissue determination relies mainly on doctors’ subjective observations and judgments and lacks objectivity. However, current deep learning models can only achieve specific discrimination using unimodal data, which limits the comprehensive understanding of scar tissue and may reduce accuracy and stability. To solve these problems, in this study, a skin scar recognition platform based on advanced deep learning and a weighted aggregation network fusion method is proposed. It is implemented using a residual network-based CNN model and a logistic regression model with L1 regularization and is suitable for both unimodal and multimodal data. The experimental results showed that the proposed platform achieved a satisfactory accuracy of 98.26% for image discrimination. In the gene discrimination model test performed on a test dataset containing 17 gene expression samples, all samples were accurately discriminated. In addition, the proposed multimodal discrimination model achieved a discrimination accuracy of 98.23%. These results validate the effectiveness of deep feature extraction and multimodal feature fusion techniques for image discrimination tasks. On this basis, to deeply explore the pathogenesis of scar formation, a method with the ability to integrate regularization, sparsity, and orthogonality constraints, multiconstraint joint non-negative matrix factorization (MCJNMF), was used to explore the genetic correlation between collagen micrographic image features and gene expression data. In this study, we confirmed the association between the calcium signaling pathway, MAPK signaling pathway, and collagen fiber repair, and successfully identified 11 potential therapeutic targets, including TRIM59 and TBC1D9, which provide important clues for future scar treatment and prevention strategies.