Remote Assessment of Eczema Severity via AI-powered Skin Image Analytics: A Systematic Review

Abstract

Various studies have been published on the remote assessment of eczema severity from digital camera images. Successful deployment of an accurate and robust AI-powered tool for such purposes can aid the formulation of eczema treatment plans and assist in patient monitoring. This review aims to provide an overview of the quality of published studies on this topic and to identify challenges and suggestions to improve the robustness and reliability of existing tools. We identified 25 articles from the Scopus database that aimed to assess eczema severity automatically from digital camera images by eczema area detection (n=13), which is important for prior delineation of the most relevant clinical features, and/or severity prediction (n=12). Deep learning methods (n=14) were more commonly used in recent years over conventional machine learning (n=11). A set of 20 pre-defined criteria were used for critical appraisal in this study. Study quality was hindered in many cases due to dataset challenges, with only 28% of studies reporting patient age range and 16% reporting skin phototype range. Furthermore, 52% of studies utilised solely non-public datasets and only 17% provided open-source access to code repositories, making validation of experimental results a significant challenge. In terms of algorithm design, attempts to improve model accuracy and process automation are widely reported. However, there remains limited implementation of methods for explicitly improving model trustworthiness and robustness. There is a need for a high-quality dataset with a sufficient number of bias-free images and consistent labels, as well as improved image analytics methods, to enhance the state of remote eczema severity assessment algorithms. Improving the interpretability and explainability of developed tools will further improve long-term reliability and trustworthiness.