Journal article
Observer-independent assessment of psoriasis-affected area using machine learning.
- Meienberger N Department of Dermatology, University Hospital Zurich, Zurich, Switzerland.
- Anzengruber F Department of Dermatology, University Hospital Zurich, Zurich, Switzerland.
- Amruthalingam L Department of Dermatology, University Hospital of Basel, Basel, Switzerland.
- Christen R Lucerne University for Applied Sciences and Arts, Lucerne, Switzerland.
- Koller T Lucerne University for Applied Sciences and Arts, Lucerne, Switzerland.
- Maul JT Department of Dermatology, University Hospital Zurich, Zurich, Switzerland.
- Pouly M Lucerne University for Applied Sciences and Arts, Lucerne, Switzerland.
- Djamei V Department of Dermatology, University Hospital Zurich, Zurich, Switzerland.
- Navarini AA Department of Dermatology, University Hospital Zurich, Zurich, Switzerland.
- 2019-10-09
Published in:
- Journal of the European Academy of Dermatology and Venereology : JEADV. - 2020
English
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-centred, interdisciplinary study of diagnostic accuracy, 259 standardized photographs of Caucasian patients were assessed and typical psoriatic lesions were labelled. Two hundred and three 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 photograph-based estimated areas by physicians was 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.
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-centred, interdisciplinary study of diagnostic accuracy, 259 standardized photographs of Caucasian patients were assessed and typical psoriatic lesions were labelled. Two hundred and three 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 photograph-based estimated areas by physicians was 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.
- Language
-
- English
- Open access status
- closed
- Identifiers
-
- DOI 10.1111/jdv.16002
- PMID 31594034
- Persistent URL
- https://sonar.ch/global/documents/133105
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