143 Automated Anatomical Mapping of Hand Eczema

Abstract

Hand eczema (HE) is a highly prevalent chronic disease affecting 15% of the population. Clinical grading systems are time-intensive and require training, making overall coarse assessment the default approach in practice. We present an image-based deep learning method to automatically evaluate HE lesion surface and determine their anatomical repartition. This retrospective study was based on two datasets: (A) 312 HE pictures and (B) 215 hand pictures. Eleven dermatologists annotated HE lesions in (A) and a student labeled (B) pictures with 37 hand anatomical sub-regions.