What we investigate
Our biomedical team specializes in replicating epidermal diseases by creating organoid-like structures derived from patient skin samples, healthy controls, and keratinocyte cell lines. Utilizing these tools, we explore the pathophysiology of these diseases and conduct drug screenings. Cutaneous images serve as the foundation for daily triage, diagnosis, and severity evaluations, inspiring our computer scientists to develop sophisticated visual classification and segmentation techniques for digital photographs. Our aim is to create decision support systems for physicians, ultimately improving the therapeutic management of dermatology patients.
KEYWORDS
inflammation, machine learning, visual diagnosis, psoriasis, eczema
Our research in more detail
Developing a high-throughput model for studying cutaneous diseases is crucial for investigating pathophysiology and performing drug screenings. Our epidermal organotypic cultures enable us to replicate the epidermis of any given patient in thousands of independent instances. We are currently using these organotypic cultures to investigate Darier's disease and the role of ER-stress in its pathophysiology.
The visual morphology of the epidermis and its underlying structures is a vital clinical feature used by dermatologists to diagnose diseases or age-related changes, determine appropriate treatments, surgeries, and other interventions, and predict the progression of pathological conditions. Visual features have been underexplored in research due to the absence of suitable techniques. Our team will contribute to SKINTEGRITY.CH by providing clinical metadata that can enhance the value of biopsy samples used in laboratory studies, which have been inadequately described thus far.
Selected publications
SKINTEGRITY.CH Principal Investigators are in bold:
- Agarwal R, Dittmar T, Beer HD, Kunz M, Kappos EA, Mueller S, Contassot E, Navarini AA. Experimental Dermatology, 2023 (accepted).
- Meienberger N, Anzengruber F, Amruthalingam L, Christen R, Koller T, Maul JT, Pouly M, Djamei V and Navarini AA (2020). Observer-independent assessment of psoriasis-affected area using machine learning. J Eur Acad Dermatol Venereol 34(6), pp. 1362-1368.
- Zhao S, Xie B, Li Y, Zhao X, Kuang Y, Su J, He X, Wu X, Fan W, Huang K, Su J, Peng Y, Navarini AA, Huang W, and Chen X (2020). Smart identification of psoriasis by images using convolutional neural networks: a case study in China. J Eur Acad Dermatol Venereol 34(3), pp. 518-524.
- Burgener AV, Bantug GR, Meyer BJ, Higgins R, Ghosh A, Bignucolo O, Ma EH, Loeliger J, Unterstab G, Geigges M, Steiner R, Enamorado M, Ivanek R, Hunziker D, Schmidt A, Müller-Durovic B, Grählert J, Epple R, Dimeloe S, Lötscher J, Sauder U, Ebnöther M, Burger B, Heijnen I, Martínez-Cano S, Cantoni N, Brücker R, Kahlert CR, Sancho D, Jones RG, Navarini A, Recher M, Hess C (2019). SDHA gain-of-function engages inflammatory mitochondrial retrograde signaling via KEAP1-Nrf2. Nat Immunol 20(10), pp. 1311-1321.