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

The role of in vivo reflectance confocal microscopy for the management of acne: A systematic review

Acne diagnosis, severity assessment and treatment follow-up rely primarily on clinical examination. In vivo reflectance confocal microscopy (RCM) provides non-invasively, real-time images of skin lesions with a level of detail close to histopathology. This systematic literature review aims to provide an overview of RCM utility in acne and a summary of specific features with clinical application that may increase objectivity in evaluating this condition. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting our results. We systematically searched three databases: PubMed, Clarivate and Google Scholar (January 2022). All included studies used RCM to investigate acne in human patients and reported the investigated skin area and type (acne lesions or clinically uninvolved skin), the substance used in the case of treatment. Our search identified 2184 records in the three databases investigated. After duplicate removal, 1608 records were screened, 35 were selected for full-text assessment, and 14 were included in this review. We used the QUADAS-2 tool to evaluate the risk of bias and applicability concerns. RCM was selected as the index test and clinical examination as the reference standard. The total number of patients from all studies was 291, with 216 acne patients and 60 healthy participants aged between 13 and 45 years. The 14 considered studies analysed 456 follicles from healthy participants, 1445 follicles from uninvolved skin in acne patients and 1472 acne lesions. Consistent RCM findings concerning follicles of acne patients reported across studies were increased follicular infundibulum size, thick, bright border, intrafollicular content and inflammation. Our analysis indicates that RCM is a promising tool for acne evaluation. Nevertheless, standardization, a unified terminology, consistent research methods and unitary reporting of RCM findings are necessary. PROSPERO registration number CRD42021266547.

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.