. Significance: Reflectance confocal microscopy (RCM) is a noninvasive, in vivo technology that offers near histopathological resolution at the cellular level. It is useful in the study of phenomena for which obtaining a biopsy is impractical or would cause unnecessary tissue damage and trauma to the patient. Aim: This review covers the use of RCM in the study of skin and the use of machine learning to automate information extraction. It has two goals: (1) an overview of information provided by RCM on skin structure and how it changes over time in response to stimuli and in disease and (2) an overview of machine learning approaches developed to automate the extraction of key morphological features from RCM images. Approach: A PubMed search was conducted with additional literature obtained from references lists. Results: The application of RCM as an in vivo tool in dermatological research and the biologically relevant information derived from it are presented. Algorithms for image classification to epidermal layers, delineation of the dermal–epidermal junction, classification of skin lesions, and demarcation of individual cells within an image, all important factors in the makeup of the skin barrier, were reviewed. Application of image analysis methods in RCM is hindered by low image quality due to noise and/or poor contrast. Use of supervised machine learning is limited by time-consuming manual labeling of RCM images. Conclusions: RCM has great potential in the study of skin structures. The use of artificial intelligence could enable an easier, more reproducible, precise, and rigorous study of RCM images for the understanding of skin structures, skin barrier, and skin inflammation and lesions. Although several attempts have been made, further work is still needed to provide a definite gold standard and overcome issues related to image quality, limited labeled datasets, and lack of phenotype variability in available databases.

Reflectance confocal microscopy (RCM) allows real-time in vivo visualization of the skin at cellular level. The study of RCM images provides information on the topological and geometrical properties of the epidermis. These may change in each layer of the epidermis, depending on the subject's age and the presence of certain dermatological conditions. Studying RCM images requires manual identification of cells to derive these properties which is timeconsuming and subject to human error, highlighting the need for an automated cell identification method. We propose an automated pipeline to analyze the structure of the skin in RCM images. The first step is to identify the region of interest (ROI) containing the epidermal cells. The second step is to identify individual cells in the segmented tissue area using an image filter. We then use prior biological knowledge to process the resulting detected cells, removing cells that are too small and reapplying the used filter locally on detected regions that are too big to be considered as a single cell. The results are evaluated both on simulated data and on manually annotated real RCM data. This study shows that automatic cell identification can be achieved, with an accuracy (precision and recall) that matches the interexpert variability.

Infant and adult skin physiology differ in many ways; however, limited data exist for older children. To further investigate the maturation processes of healthy skin during childhood. Skin parameters were recorded in 80 participants of four age groups: babies (0–2 years), young children (3–6 years), older children (7–<10 years) and adults (25–40 years). Overall, skin barrier function continues to mature, reaching adult levels of transepidermal water loss (TEWL), lipid compactness, stratum corneum (SC) thickness and corneocyte size by the age of about 6 years. Higher levels of lactic acid and lower levels of total amino acids in the SC of babies and young children further indicate higher cell turnover rates. In all age groups, TEWL and skin surface hydration values remain higher on the face compared with the arm. Skin becomes darker and contains higher levels of melanin with increasing age. The composition of skin microbiome of the dorsal forearm in all children groups is distinct from that in adults, with Firmicutes predominating in the former and Proteobacteria in the latter. Skin physiology, along with the skin microbiome, continues to mature during early childhood in a site‐specific manner.

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