Diascopic LLC, a Cleveland-based medical research company using diagnostic technology developed at Case Western Reserve University, will use a highly competitive $225,000 federal grant to develop and apply new artificial intelligence (AI) and digital pathology tools for detecting tuberculosis (TB).
Company principals Cary Serif, chief executive officer, and Jim Uhlir, vice president of research and engineering, are focused on rapid, mobile, low-cost and accurate digital pathology solutions.
The company has conducted preliminary studies in five African countries where TB is prevalent. Preliminary results were promising enough to solicit Small Business Innovation Research (SBIR) funding from the National Institute of Health’s National Institute for Biomedical Imaging and Bioengineering.
Serif and Anant Madabhushi, the F. Alex Nason Professor II of Biomedical Engineering in the Department of Biomedical Engineering at the Case School of Engineering, are co-principal investigators on the grant.
Madabhushi, also director of Case Western Reserve’s Center for Computational Imaging and Personalized Diagnostics, brings his image-analysis expertise to augment the Diascopic platform by applying AI to help classify TB bacterium within the images.
Diascopic combines low-magnification, high-resolution imaging with digital-analysis software for a portable, simple and flexible digital diagnostic platform that allows for immediate inspection of microscopic specimens.
Health care specialists need a faster, more reliable disease detection process that functions as well in the lab as it does for mobile applications. In addition, a new level of automation can provide economic operating efficiencies.
Diascopic is working with the Uganda Case Research Collaboration (UCRC), a subcontractor on the SBIR grant, to collect specimens and prepare slides and bacterial cultures in Uganda.
The highly competitive federal SBIR program encourages domestic small businesses to engage in research and development with potential for commercialization.
From 2011 through 2014, Uhlir, a Case Western Reserve double alum and an engineering manager at the Case School of Engineering, and Serif performed studies in four clinics across South Africa, Namibia and Uganda to train the software platform. From those studies, the accuracy rate for detection rose from 75% to 95%.
“The intention,” Uhlir said, “is to raise the bar, compared to molecular and culture diagnostic tests, to reduce cost and technical skill and increase the speed of diagnosis.”