Detecting Eczema Areas in Digital Images: An Impossible Task?
AD, atopic dermatitis
ICC, intraclass correlation coefficient
IRR, inter-rater reliability
KA, Krippendorff’s alpha
ML, machine learning
Journal
JID innovations : skin science from molecules to population health
ISSN: 2667-0267
Titre abrégé: JID Innov
Pays: Netherlands
ID NLM: 101776173
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
received:
19
10
2021
revised:
28
04
2022
accepted:
02
05
2022
entrez:
12
9
2022
pubmed:
13
9
2022
medline:
13
9
2022
Statut:
epublish
Résumé
Assessing the severity of atopic dermatitis (AD, or eczema) traditionally relies on a face-to-face assessment by healthcare professionals and may suffer from inter- and intra-rater variability. With the expanding role of telemedicine, several machine learning algorithms have been proposed to automatically assess AD severity from digital images. Those algorithms usually detect and then delineate (segment) AD lesions before assessing lesional severity and are trained using the data of AD areas detected by healthcare professionals. To evaluate the reliability of such data, we estimated the inter-rater reliability of AD segmentation in digital images. Four dermatologists independently segmented AD lesions in 80 digital images collected in a published clinical trial. We estimated the inter-rater reliability of the AD segmentation using the intraclass correlation coefficient at the pixel and the area levels for different resolutions of the images. The average intraclass correlation coefficient was 0.45 (
Identifiants
pubmed: 36090300
doi: 10.1016/j.xjidi.2022.100133
pii: S2667-0267(22)00041-8
pmc: PMC9460154
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100133Subventions
Organisme : Medical Research Council
ID : MC_PC_19040
Pays : United Kingdom
Informations de copyright
© 2022 The Authors.
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