Overview
The SkincAIr project develops an AI-supported mobile application to improve the early detection and monitoring of skin-related neglected tropical diseases (skin NTDs) in sub-Saharan Africa. These diseases often affect communities with limited access to dermatological expertise and diagnostic resources, where delayed diagnosis can contribute to disease progression and long-term health consequences.
At the core of SkincAIr is an artificial intelligence system that analyses photographs of skin conditions acquired with smartphones. The system is designed to support front-line healthcare workers in identifying skin NTDs and assessing disease characteristics in remote and resource-limited settings. The project is implemented in Kenya, Senegal, Ethiopia, Nigeria, and the Democratic Republic of the Congo.
A major component of the project is the creation of a diverse and quality-controlled image dataset, with particular attention to populations and darker skin tones that are underrepresented in existing dermatological AI resources. These data will support the development and evaluation of machine-learning models under real-world conditions.
The Lucerne University of Applied Sciences and Arts (HSLU) contributes to the development of AI models for image analysis, with a focus on robustness across different skin types, image acquisition conditions, and populations.
Beyond individual diagnosis, SkincAIr also aims to support disease monitoring, epidemiological surveillance and mapping. The project follows a user-centered approach, involving healthcare workers and local stakeholders, and aims to deliver an accessible, interoperable, and scalable digital health solution for regions where specialist dermatological services are limited.
