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Title:

Automated classification of hidradenitis suppurativa disease severity by convolutional neural network analyses using calibrated clinical images.

Document type:
Journal Article
Author(s):
Wiala, A; Ranjan, R; Schnidar, H; Rappersberger, K; Posch, C
Abstract:
BACKGROUND: The assessment of hidradenitis suppurativa (HS) severity requires detailed, and error-prone lesion counts. This proof-of-concept study aimed to automatically classify HS disease severity using machine learning of clinical smartphone images. METHODS: 777 ambient-light and size-controlled images were used to build a class-balanced synthetic dataset (n = 7675). Convolutional neural networks (CNN) were used for automated severity classification (scale 0-3), and to assess disease-dynamics...     »
Journal title abbreviation:
J Eur Acad Dermatol Venereol
Year:
2024
Journal volume:
38
Journal issue:
3
Pages contribution:
576-582
Fulltext / DOI:
doi:10.1111/jdv.19639
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/38013510
Print-ISSN:
0926-9959
TUM Institution:
Klinik und Poliklinik für Dermatologie und Allergologie (Prof. Biedermann)
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