Objective hand eczema severity assessment with automated lesion anatomical stratification

Ludovic Amruthalingam, Nora Mang, Philippe Gottfrois, Alvaro Gonzalez-Jimenez, Julia-Tatjana Maul, Michael Kunz, Marc Pouly, Alexander Navarini

Abstract:

Hand eczema (HE) is one of the most frequent dermatoses, known to be both relapsing
and remitting. Regular and precise evaluation of the disease severity is key for
treatment management. Current scoring systems such as the hand eczema severity
index (HECSI) suffer from intra-and inter-observer variance. We propose an automated
system based on deep learning models (DLM) to quantify HE lesions' surface
and determine their anatomical stratification. In this retrospective study, a team of
11 experienced dermatologists annotated eczema lesions in 312 HE pictures, and a
medical student created anatomical maps of 215 hands pictures based on 37 anatomical
subregions. Each data set was split into training and test pictures and used to train
and evaluate two DLMs, one for anatomical mapping, the other for HE lesions segmentation.
On the respective test sets, the anatomy DLM achieved average precision
and sensitivity of 83% (95% confidence interval [CI] 80–85) and 85% (CI 82–88),
while the HE DLM achieved precision and sensitivity of 75% (CI 64–82)
and 69% (CI 55–81).
The intraclass correlation of the predicted HE surface with dermatologists' estimated
surface was 0.94 (CI 0.90–0.96). The proposed method automatically predicts the
anatomical stratification of HE lesions' surface and can serve as support to evaluate
hand eczema severity, improving reliability, precision and efficiency over manual assessment.
Furthermore, the anatomical DLM is not limited to HE and can be applied to
any other skin disease occurring on the hands such as lentigo or psoriasis.