Project description
The app combines machine learning and spectral images to assist with lesion recognition instruction through advanced diagnostic training. It is specifically designed for use in resource-limited environments and by healthcare professionals with limited access to traditional diagnostic tools. The project is built on a unique database of 24,000 clinically assessed images, supported by research from the European Research Council-funded DUALSAVE-FGS project and the World Health Organization (WHO).
The primary goals are to transition the app from the prototype stage (TRL 4) to operational deployment (TRL 7), validate its diagnostic accuracy, and implement it in healthcare institutions in Norway, South Africa, and Uganda. Further objectives include training healthcare workers, establishing a fair and sustainable business model, and contributing to the UN Sustainable Development Goal 3 for good health and well-being as well as WHO's Universal Health Coverage (UHC). This should ensure everyone has access to quality essential health services without financial hardship.
Research and development challenges include ensuring diagnostic precision across diverse clinical environments, achieving user acceptance, and integrating the app into existing health systems. The project employs interdisciplinary approaches, combining digital health, public health, machine learning, and clinical research to tackle these challenges.
