Non-Communicable Diseases (NCDs) such as cardiovascular disease, diabetes and cancer are a major burden in Uganda, increasing the need for requisite diagnosis/therapeutic procedures such as vascular interventions and biopsies. These procedures involve percutaneous needle insertion, and their success relies on accurate needle localization. Ultrasound imaging is the gold-standard for visualization of needle progress vis-à-vis patient anatomy. However, under ultrasound, needle localization is hindered by signal attenuation, limited field of view and artifacts. Inaccurate needle localization reduces procedure efficacy and can cause injury. In this project, these problems are addressed by developing a low-cost imaging system for diagnosis/treatment of NCDs that uses machine learning for needle localization.
