Reliable detection of eczema areas for fully automated assessment of eczema severity from digital camera images

Assessing the severity of eczema in clinical research requires face-to-face skin examination by trained staff. Such approaches are resource-intensive for participants and staff, challenging during pandemics, and prone to inter- and intra-observer variation. Computer vision algorithms have been proposed to automate the assessment of eczema severity using digital camera images. However, they often require human intervention to detect eczema lesions and cannot automatically assess eczema severity from real-world images in an end-to-end pipeline. We developed a new model to detect eczema lesions from images using data augmentation and pixel-level segmentation of eczema lesions on 1345 images provided by dermatologists. We evaluated the quality of the obtained segmentation compared to that of the clinicians, the robustness to varying imaging conditions encountered in real-life images, such as lighting, focus, and blur and the performance of downstream severity prediction when using the detected eczema lesions. The quality and robustness of eczema lesion detection increased by approximately 25% and 40%, respectively, compared to our previous eczema detection model. The performance of the downstream severity prediction remained unchanged. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by British Skin Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Softened Water Eczema Trial (SWET) (Thomas et al., 2011) study was approved by North West Research Ethics Committee (Ref 06/MRE08/77) and written informed consent was provided by the parent/caregiver of participating children. The secondary use of the data was approved by Science Engineering Technology Research Ethics Committee at Imperial College London (SETREC number 22IC7801). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Source code is publicly available at https://github.com/Tanaka-Group/EczemaNet2.