Deepnapsi: Deep Learning for Nail Psoriasis Prediction

Folle L, Fenzl P, Fagni F, Thies M, Christlein V, Meder C, Sticherling M, Simon D, Schett G, Maier A, Kleyer A (2023)

Publication Type: Conference contribution

Publication year: 2023


Publisher: IEEE Computer Society

Book Volume: 2023-April

Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging

Event location: Cartagena CO

ISBN: 9781665473583

DOI: 10.1109/ISBI53787.2023.10230472


Nail psoriasis is a frequent condition that is associated with a severe course of rheumatic diseases. Thus, it may be used to adapt the therapy of patients according to the change of nail psoriasis. The nail psoriasis severity index (NAPSI) was developed to measure this condition. Nevertheless, there is a lack of application for the NAPSI, as its use in the clinic is too time-consuming. In this work, we propose the use of advancements in deep learning in a new domain by recording, annotating, and predicting the NAPSI based on photographs of the hand and term our approach DeepNAPSI. This allows not just the automated recording of the NAPSI in the clinic, but also patient self-assessment from home. Our method achieved an area-under-receiver-operator-characteristic curve (AUROC) of 0.83 and 0.86 for macro and micro averaging, respectively, and a mean absolute error of 0.55.

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How to cite


Folle, L., Fenzl, P., Fagni, F., Thies, M., Christlein, V., Meder, C.,... Kleyer, A. (2023). Deepnapsi: Deep Learning for Nail Psoriasis Prediction. In Proceedings - International Symposium on Biomedical Imaging. Cartagena, CO: IEEE Computer Society.


Folle, Lukas, et al. "Deepnapsi: Deep Learning for Nail Psoriasis Prediction." Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023, Cartagena IEEE Computer Society, 2023.

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