129 Fully automated assessment of Atopic Dermatitis severity from real-world digital images

Abstract

Assessing the severity of Atopic Dermatitis (AD) traditionally relies on face-to-face assessments by healthcare professionals. Such approaches are resource-intensive for participants and staff, challenging during pandemics, and prone to inter- and intra-observer variation. We aim to investigate to what extent computer vision algorithms can help standardise and automate the detection and assessment of AD severity using real-world digital images, without human intervention. We developed EczemaNet, a deep learning computer vision pipeline to detect and assess AD severity from digital camera images.