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Observer‐independent assessment of psoriasis affected area using machine learning


Meienberger, N; Anzengruber, F; Amruthalingam, L; Christen, R; Koller, T; Maul, J T; Pouly, M; Djamei, V; Navarini, A A (2020). Observer‐independent assessment of psoriasis affected area using machine learning. Journal of the European Academy of Dermatology and Venerology, 34(6):1362-1368.

Abstract

Background

Assessment of psoriasis severity is strongly observer‐dependent and objective assessment tools are largely missing. The increasing number of patients receiving highly expensive therapies that are reimbursed only for moderate‐to‐severe psoriasis motivates the development of higher quality assessment tools.
Objective

To establish an accurate and objective psoriasis assessment method based on segmenting images by machine learning technology.
Methods

In this retrospective, non‐interventional, single‐centered, interdisciplinary study of diagnostic accuracy 259 standardized photographs of Caucasian patients were assessed and typical psoriatic lesions were labelled. 203 of those were used to train and validate an assessment algorithm which was then tested on the remaining 56 photographs. The results of the algorithm assessment were compared with manually marked area, as well as with the affected area determined by trained dermatologists.
Results

Algorithm assessment achieved accuracy of more than 90% in 77% of the images and differed on average 5.9% from manually marked areas. The difference between algorithm predicted and photo based estimated areas by physicians were 8.1% on average.
Conclusion

The study shows the potential of the evaluated technology. In contrast to the Psoriasis Area and Severity Index (PASI) it allows for objective evaluation and should therefore be developed further as an alternative method to human assessment.

Abstract

Background

Assessment of psoriasis severity is strongly observer‐dependent and objective assessment tools are largely missing. The increasing number of patients receiving highly expensive therapies that are reimbursed only for moderate‐to‐severe psoriasis motivates the development of higher quality assessment tools.
Objective

To establish an accurate and objective psoriasis assessment method based on segmenting images by machine learning technology.
Methods

In this retrospective, non‐interventional, single‐centered, interdisciplinary study of diagnostic accuracy 259 standardized photographs of Caucasian patients were assessed and typical psoriatic lesions were labelled. 203 of those were used to train and validate an assessment algorithm which was then tested on the remaining 56 photographs. The results of the algorithm assessment were compared with manually marked area, as well as with the affected area determined by trained dermatologists.
Results

Algorithm assessment achieved accuracy of more than 90% in 77% of the images and differed on average 5.9% from manually marked areas. The difference between algorithm predicted and photo based estimated areas by physicians were 8.1% on average.
Conclusion

The study shows the potential of the evaluated technology. In contrast to the Psoriasis Area and Severity Index (PASI) it allows for objective evaluation and should therefore be developed further as an alternative method to human assessment.

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4 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Dermatology Clinic
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Dermatology
Health Sciences > Infectious Diseases
Uncontrolled Keywords:Infectious Diseases, Dermatology
Language:English
Date:1 June 2020
Deposited On:15 Jan 2020 14:23
Last Modified:08 Oct 2020 00:00
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0926-9959
OA Status:Green
Publisher DOI:https://doi.org/10.1111/jdv.16002
PubMed ID:31594034

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