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Clinical success of regenerative medicine for treating deep‐tissue skin injuries depends on the availability of skin grafts. Though bioengineered constructs are tested clinically, lack of neovascularization provide only superficial healing. Thus constructs, which promotes wound healing and supports vascularization has gained priority in tissue engineering. In this study, chitosan‐collagen‐fibrinogen (CCF) scaffold was fabricated using freeze‐drying method without using any chemical crosslinkers. CCF scaffolds proved cytocompatibility and faster healing in in vitro scratch assay of primary human adult dermal fibroblasts cells with progressively increasing vascular endothelial growth factor‐A and reducing vascular endothelial growth factor receptor 1 expressions. Skin regeneration evaluated on in vivo full thickness wound model confirmed faster remodeling with angiogenic signatures in CCF scaffold‐implanted mice. Histopathological observations corroborated with stereo‐zoom and SS‐optical coherence tomography images of wound sites to prove the maturation of healing‐bed, after 12 days of CCF implantation. Therefore, it is concluded that CCF scaffolds are promising for skin tissue regeneration and demonstrates pro‐angiogenic potential. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) is a medical imaging modality that allows us to probe deeper sub-structures of skin. The state-of-the-art wound care prediction and monitoring methods are based on visual evaluation and focus on surface information. However, research studies have shown that sub-surface information of the wound is critical for understanding the wound healing progression. This work demonstrated the use of OCT as an effective imaging tool for objective and non-invasive assessments of wound severity, the potential for healing, and healing progress by measuring the optical characteristics of skin components. We have demonstrated the efficacy of OCT in studying wound healing progress in vivo small animal models. Automated analysis of OCT datasets poses multiple challenges, such as limitations in the training dataset size, variation in data distribution induced by uncertainties in sample quality and experiment conditions. We have employed a U-Net-based model for segmentation of skin layers based on OCT images and to study epithelial and regenerated tissue thickness wound closure dynamics and thus quantify the progression of wound healing. In the experimental evaluation of the OCT skin image datasets, we achieved the objective of skin layer segmentation with an average intersection over union (IOU) of 0.9234. The results have been corroborated using gold-standard histology images and co-validated using inputs from pathologists.Clinical Relevance-To monitor wound healing progression without disrupting the healing procedure by superficial, non-invasive means via the identification of pixel characteristics of individual layers.