Sickle Cell Disease
Sickle Cell Disease is a genetic disorder that affects 30 million people worldwide, including over 100,000 Americans. It causes excruciating pain, and severe complications such as chronic organ damage, blindness, and stroke.
In the United States, individuals living with sickle cell, who are mostly in the African American and Hispanic communities, live on average 33 years shorter lives than the average American. Frequent emergency room visits and hospitalization causes significant economic, social, and psychological burdens. Sickle Cell Disease exacerbates the already stark disparity in health care access and outcomes for African Americans as compared to the rest of the population. The total lifetime treatment cost for a patient ranges between $1M and $9M, owing in large part to hospitalizations and emergency care.
At KovaDx, we're on a mission to provide actionable insights that will improve the quality of life of people living with Sickle Cell Disease.
KovaDx is building a tool that will allow for the ongoing monitoring and preventative care for individuals with Sickle Cell Disease by using 3D Quantitative Phase Imaging to obtain data about cells. This data is run through our proprietary deep learning models for a monitoring classification based on cell features.
Our tool has platform applicability and will also perform the Complete Blood Count test, the most common test ordered in the world. In addition to people living with Sickle Cell Disease, CBC will be useful for patients with other conditions who need blood monitoring at home.
Park H-S, Price H, Ceballos S, Chi J-T, Wax A. Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry. Cells. 2021; 10(9):2455.
We care deeply about addressing systemic injustices that deprive people of basic rights. We believe that regardless of income or circumstances of birth, no one should have to forgo healthcare due to a lack of resources. Starting with Sickle Cell Disease, we will apply our proprietary 3D phase imaging microscope and deep learning algorithms to solve the world’s biggest health problems.