Diagnosing Tuberculosis

Principal Investigator:
Yutaka Yasui

Problem we’re trying to solve

Inexpensive, timely and accurate diagnosis of potential cases of Tuberculosis is of critical importance in regions of the world where resources are limited. Standard methods of diagnosis are often too expensive or resource intensive to be deployed in the very regions where tuberculosis is a significant problem. This leads to poor patient outcomes due to delayed treatment and undiagnosed illness.

How will this help someone / an industry?

The machine learning component of this work developed a new automated diagnostic method using image analysis which enables diagnosis that is more efficient and lower cost than standard methods, and because it is automated, poses lower biohazard risk to the technicians processing samples.


TV/HIV Research Foundation (Thailand)