Skin surface biomarkers are associated with future development of atopic dermatitis in children with family history of allergic disease.

BACKGROUND: Atopic dermatitis (AD) is a common childhood chronic inflammatory skin disorder that can significantly impact quality of life and has been linked to the subsequent development of food allergy, asthma, and allergic rhinitis, an association known as the "atopic march." OBJECTIVE: The aim of this study was to identify biomarkers collected non-invasively from the skin surface in order to predict AD before diagnosis across a broad age range of children. METHODS: Non-invasive skin surface measures and biomarkers were collected from 160 children (3-48 months of age) of three groups: (A) healthy with no family history of allergic disease, (B) healthy with family history of allergic disease, and (C) diagnosed AD. RESULTS: Eleven of 101 children in group B reported AD diagnosis in the subsequent 12 months following the measurements. The children who developed AD had increased skin immune markers before disease onset, compared to those who did not develop AD in the same group and to the control group. In those enrolled with AD, lesional skin was characterized by increased concentrations of certain immune markers and transepidermal water loss, and decreased skin surface hydration. CONCLUSIONS: Defining risk susceptibility before onset of AD through non-invasive methods may help identify children who may benefit from early preventative interventions.

Skin maturation from birth to 10 years of age: Structure, function, composition and microbiome.

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.

Age-dependent changes in epidermal architecture explored using an automated image analysis algorithm on in vivo reflectance confocal microscopy images.

BACKGROUND: Reflectance confocal microscopy (RCM) allows for real-time in vivo visualization of the epidermis at the cellular level noninvasively. Parameters relating to tissue architecture can be extracted from RCM images, however, analysis of such images requires manual identification of cells to derive these parameters, which can be time-consuming and subject to human error, highlighting the need for an automated cell identification method. METHODS: First, the region-of-interest (ROI) containing cells needs to be identified, followed by the identification of individual cells within the ROI. To perform this task, we use successive applications of Sato and Gabor filters. The final step is post-processing improvement of cell detection and removal of size outliers. The proposed algorithm is evaluated on manually annotated real data. It is then applied to 5345 images to study the evolution of epidermal architecture in children and adults. The images were acquired on the volar forearm of healthy children (3 months to 10 years) and women (25-80 years), and on the volar forearm and cheek of women (40-80 years). Following the identification of cell locations, parameters such as cell area, cell perimeter, and cell density are calculated, as well as the probability distribution of the number of nearest neighbors per cell. The thicknesses of the Stratum Corneum and supra-papillary epidermis are also calculated using a hybrid deep-learning method. RESULTS: Epidermal keratinocytes are significantly larger (area and perimeter) in the granular layer than in the spinous layer and they get progressively larger with a child's age. Skin continues to mature dynamically during adulthood, as keratinocyte size continues to increase with age on both the cheeks and volar forearm, but the topology and cell aspect ratio remain unchanged across different epidermal layers, body sites, and age. Stratum Corneum and supra-papillary epidermis thicknesses increase with age, at a faster rate in children than in adults. CONCLUSIONS: The proposed methodology can be applied to large datasets to automate image analysis and the calculation of parameters relevant to skin physiology. These data validate the dynamic nature of skin maturation during childhood and skin aging in adulthood.

Automatic granular and spinous epidermal cell identification and analysis on in vivo reflectance confocal microscopy images using cell morphological features.

Significance: Reflectance confocal microscopy (RCM) allows for real-time in vivo visualization of the skin at the cellular level. The study of RCM images provides information on the structural 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 time consuming and subject to human error, highlighting the need for an automated cell identification method. Aim: We aim to design an automated pipeline for the analysis of the structure of the epidermis from RCM images of the Stratum granulosum and Stratum spinosum. Approach: We identified the region of interest containing the epidermal cells and the individual cells in the segmented tissue area using tubeness filters to highlight membranes. We used prior biological knowledge on cell size to process the resulting detected cells, removing cells that were too small and reapplying the used filters locally on detected regions that were too big to be considered a single cell. The proposed full image analysis pipeline (FIAP) was compared with machine learning-based approaches (cell cutter, different U-Net configurations, and loss functions). Results: All methods were evaluated both on simulated data (four images) and on manually annotated RCM data (seven images). Accuracy was measured using recall and precision metrics. Both accuracy metrics were higher in the proposed FIAP for both real ( precision = 0.720 ± 0.068 , recall = 0.850 ± 0.11 ) and synthetic images ( precision = 0.835 ± 0.067 , recall = 0.925 ± 0.012 ). The tested machine learning methods failed to identify and segment keratinocytes on RCM images with a satisfactory accuracy. Conclusions: We showed that automatic cell segmentation can be achieved using a pipeline based on membrane detection, with an accuracy that matches expert manual cell identification. To our knowledge, this is the first method based on membrane detection to study healthy skin using RCM images evaluated against manually identified cell positions.

Automating reflectance confocal microscopy image analysis for dermatological research: a review.

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.