Estimation of skin surface roughness in vivo based on optical coherence tomography combined with convolutional neural network - Zhang, Zhiqun; Chen, Zhida; Li, Zhenqian; Zou, Jian; Guo, Jian; Chen, Kaihong; Guo, Yong; Li, Zhifang (Frontiers in Medicine, 2024)
The texture of human skin is influenced by both external and internal factors, and changes in wrinkles can most directly reflect the state of the skin. Skin roughness is primarily used to quantify the wrinkle features of the skin. Therefore, it is important to have effective and accurate quantification of skin roughness in the fields of skincare, medical treatment, and product development. In this study, the method is proposed for estimating the skin surface roughness based on optical coherence tomography (OCT) combined with convolutional neural network (CNN). The proposed algorithm is validated through a roughness standard plate. Then the experimental results revealed that the skin surface roughness including the arithmetic mean roughness and depth of roughness depend on age and gender. The advantage of the proposed method based on OCT can reduce the effect of the skin surface's natural curvature on roughntess. In addition, the method is combined with the epidermal thickness and dermal attenuation coefficient for multi-parameters characterization of skin features. It could be seen as a potential tool in understanding the aging process and developing strategies to maintain and enhance skin health and appearance.