"Advances in modern medicine and technology have made it possible to deliver equitable, high-quality information for all patients, regardless of where they live or seek medical attention. With this partnership, we are developing decision support tools to assist primary healthcare providers working in low-resource settings."
VisualDx is grateful to have been awarded a grant from the Bill & Melinda Gates Foundation to provide support to ongoing national public health efforts in India and Nigeria by developing country-specific logic to enhance surveillance, detection, and triage of neglected tropical diseases and other rare infectious diseases.
This grant will allow VisualDx to do our part in advancing global health and diagnostic accuracy in underserved regions.
Health-related decisions are often delivered around the world by providers who lack the resources and specialized knowledge required to optimally deliver quality outcomes, particularly with respect to neglected tropical diseases (NTDs).
Early identification of these diseases is hindered by the lack of knowledge about these conditions, their symptoms, and treatment options.
Whether the first point of care for patients is in the community, the pharmacy, the clinic, or other facilities, there is a clear need for actionable decision support and education.
- Development of a prototype clinical decision support and surveillance tool focused on post-kala-azar dermal Leishmaniasis (PKDL)
- Using VisualDx will remove existing barriers by enabling healthcare workers to evaluate skin lesions and symptoms, understand diagnostic possibilities, learn about the diseases, and obtain guidance on the next steps for clinical testing and treatment.
- Development of a prototype clinical decision support and surveillance tool focused on image collection and machine learning algorithmic analysis optimization for a selection of NTDs in Nigeria.
- This prototype CDSS will identify representative images of NTD presentations and utilize these images to train machine learning algorithms for image analysis to distinguish between NTDs and NTD mimics, improving diagnostic accuracy and timeliness.
This health initiative is supported by: