Project Lead: Coleman Martin, UW Chemical Engineering

Data Science Lead: Noah Benson

In 2014 the United Nations set out to end the HIV/AIDs endemic by 2030 with the goal of having 90% of people living with HIV know their HIV status, be on retroviral treatment, and be virally suppressed. As of 2019, the most recent data, the United Nations program on HIV/AIDs reports that only 59% of people living with HIV are virally suppressed. HIV viral load testing is the key diagnostic in this plan; to achieve these goals, 30 million diagnostic tests will be needed each year. Yet a rapid, quantitative, and accessible diagnostic test is not currently available.

The Posner Research Group has developed a novel rapid quantitative DNA test that relies on discrete puncta counting in images. However, this method loses accuracy at high viral loads within the clinically relevant range. In this work, we aim to extend the dynamic range of this diagnostic test through the use of spatial temporal data and higher order models such at ResNets. In doing so, we aim to improve the quantitative accuracy across the dynamic range of this system and bring this method closer to clinical utility.