Observer-independent assessment of psoriasis affected area using machine learning

Meienberger, N. and Anzengruber, F. and Amruthalingam, L. and Christen, R. and Koller, T. and Maul, J. T. and Pouly, M. and Djamei, V. and Navarini, A. A.. (2019) Observer-independent assessment of psoriasis affected area using machine learning. Journal of the European Academy of Dermatology and Venereology : JEADV, 34 (6). pp. 1362-1368.

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Official URL: https://edoc.unibas.ch/72180/

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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.; To establish an accurate and objective psoriasis assessment method based on segmenting images by machine learning technology.; 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.; 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.; 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.
Faculties and Departments:03 Faculty of Medicine > Bereich Spezialf├Ącher (Klinik) > Dermatologie USB > Dermatologie (Navarini)
03 Faculty of Medicine > Departement Klinische Forschung > Bereich Spezialf├Ącher (Klinik) > Dermatologie USB > Dermatologie (Navarini)
UniBasel Contributors:Navarini, Alexander
Item Type:Article, refereed
Article Subtype:Research Article
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:09 Jul 2020 15:25
Deposited On:09 Jul 2020 15:25

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