Hand eczema is a frequent dermatosis with severe health and financial consequences to patients and society. It follows a chronic course and persists up to 15 years after onset. Early detection of an exacerbation followed by the application of specific drugs for a few days can considerably reduce disease activity and avoid temporary disability. However, dermatitis patients usually rely on their own perception in assessing their skin condition and therefore often miss the time point for effective treatment. In this paper we present a prototype-based feasibility study of automated detection and quantification of hand eczema using texton-based imaging and machine-learning techniques.
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