Diascopic’s AI For TB Detection Secures $225,000 Small Business Innovation Research Grant

Diascopic's iON platform detects the tuberculosis bacterium digitally in less than 60 seconds from a single sample. Source: Diascopic, LLC
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Diascopic LLC, a medical research company that develops diagnostic technology has secured $225,000 of highly competitive Small Business Innovation Research (SBIR) funding from the National Institute of Health’s National Institute for Biomedical Imaging and Bioengineering. It will use the funds to develop and apply new artificial intelligence (AI) and digital pathology tools for detecting tuberculosis (TB).

Diascopic LLC is a Cleveland, Ohio, based medical diagnostic research company. Since 2009, Diascopic has been focused on developing a robust platform capable of using the power and intricacies of digital analysis to define, recognize and identify significant pathogens. The company seeks to reduce the cost and technical skill of diagnostic tests while increasing the speed and accuracy of diagnosis, in order to allow for near point-of-care results in high-burden, low-resource environments.

According to the Word Health Organization, an estimated 1.8 billion people, that’s almost one quarter of the world’s population, are currently infected with the bacterium that causes TB. It is found in every country in the world, and is the leading infectious cause of death worldwide. In 2018, according to the TB Alliance, 10 million individuals fell ill from TB, and 1.6 million died.

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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.

Unlike traditional methods for TB diagnosis, which require highly trained technicians, laboratory equipment and several hours to several days, Diascopic’s iON platform detects the TB bacterium digitally in less than 60 seconds from a single sample.

From 2011 through 2014, company principals Cary Serif and Jim Uhlir performed studies in four clinics across South Africa, Namibia and Uganda to train the AI software platform, which required large numbers of biospecimens. From those studies, the accuracy rate for detection rose from 75% to 95%.

Diascopic is working with the Uganda Case Research Collaboration (UCRC), a collaborator on the SBIR grant, to collect 400 specimens, from which Diascopic will generate roughly 60,000 digital images.

“By digitizing the process, we’re able to reuse the image in perpetuity, which makes the test highly repeatable.” … “Just as importantly, the digitization allows us to build a massive reference library to which we can apply artificial intelligence and data analytics to continue improving the test’s accuracy.” – Cary Serif, Chief Executive Officer, Diascopic

“The intention, is to raise the bar, compared to other diagnostic tests, to reduce the cost and technical skill needed to administer the test, while also increasing the speed of diagnosis.” – Jim Uhlir, Vice President, Research and Engineering, Diascopic


  1. https://bioengineer.org/startup-developing-ai-for-tb-detection-secures-federal-business-innovation-grant/