eSSENCE@LU 8:7 - Breast cancer diagnosis using pocket-sized ultrasound device and Deep Learning

Breast cancer is one of the most commonly occurring cancer among women, and while screening work relatively well for early detection in high-income countries, there is no corresponding solution in low- and middle-income countries. The limited access to early diagnosis and treatment leads to that the survival in breast cancer is much lower. Both competence and equipment are lacking, and it does not seem likely that the breast cancer care can be copied from high-income countries. Therefore, we suggest the development of an accessible breast diagnostic tool, consisting of a technically enhanced low-cost pocked ultrasound device paired with a smartphone-based artificial intelligence algorithm. The goal of this project is to develop such an algorithm, using existing data from Skåne University Hospital and tuning it such that it also works well when the pocket ultrasound is used.