Record link: 
http://hdl.handle.net/20.500.12708/231129
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Title: 
Artificial intelligence (AI)-powered imaging for objective psoriasis severity assessment at home: Feasibility and reliability in a randomized clinical trial
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Citation: 
Oliver, M., Molnarova, M., Bergmans, M., Ranjan, R., Exadaktylos, V., Niemeyer van der Kolk, T., Seyger, M. M. B., Wee, L., Balak, D., van Doorn, M., Rissmann, R., Schnidar, H., Rousel, J., Vreeken, R., Cuypers, E., van Beugen, S., van Laarhoven, A., Lelieveldt, B., El Ghalbzouri, A., … The Next Generation ImmunoDermatology Consortium (NGID). (2026). Artificial intelligence (AI)-powered imaging for objective psoriasis severity assessment at home: Feasibility and reliability in a randomized clinical trial. Journal of The American Academy of Dermatology, 95(4), 1182–1184. https://doi.org/10.1016/j.jaad.2026.06.047
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Publisher DOI: 
10.1016/j.jaad.2026.06.047
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Oliver, Mariona 
Molnarova, Monika 
Bergmans, Menthe 
Ranjan, Rahul 
Exadaktylos, Vasileios 
Niemeyer van der Kolk, Tessa 
Seyger, Marieke M.B. 
Wee, Leonard 
Balak, Deepak 
van Doorn, Martijn 
Rissmann, Robert 
Schnidar, Harald 
Rousel, Jannik 
Vreeken, Rob 
Cuypers, Eva 
van Beugen, Sylvia 
van Laarhoven, Antoinette 
Lelieveldt, Boudewijn 
El Ghalbzouri, Abdoel 
van den Reek, Juul 
van den Bogaard, Ellen 
de Jong, Elke 
van den Broek, Peter 
Arents, Bernd 
de Leeuw, Ton 
Ederveen, Tom 
Bram ’t Hoen, Peter 
Sluijmers, Annemarie 
Arents, Bernd 
de Leeuw, Mieke 
de Leeuw, Ton 
van den Broek, Peter 
van der Pool, Els 
Wezendonk, Lisanne 
Vellinga, Douwe 
Weidinger, Stephan 
Dubrak, Sandrine 
Li, Yang 
Steensel, Maurice 
Marchetti-Deschmann, Martina 
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Author Collaboration: 
The Next Generation ImmunoDermatology Consortium (NGID)
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Organisational Unit: 
E164-01-1 - Forschungsgruppe Massenspektrometrische Bio- und Polymeranalytik 
E056-04 - Fachbereich TU-DX: Towards Applications of 2D Materials 
E056-12 - Fachbereich ENROL DP 
E056-14 - Fachbereich Mature Tissue 
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Journal: 
Journal of The American Academy of Dermatology 
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ISSN: 
0190-9622
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Date (published): 
Oct-2026
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Number of Pages: 
3
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Publisher: 
MOSBY-ELSEVIER
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Peer reviewed: 
Yes
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Keywords: 
artificial intelligence; convolutional neural network; dermatology; machine learning; medical imaging; objective assessment; psoriasis; psoriasis area and severity index; severity assessment; smartphone; target lesion score
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Abstract: 
Key Message: Patient-captured smartphone images analyzed by AI, reliably detect and quantify treatment-related changes consistent with physician scoring.
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Research Areas: 
Materials Characterization: 50%
Biological and Bioactive Materials: 50%
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Science Branch: 
1040 - Chemie: 100%
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