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At the Kenya Medical Research Institute, research is underway to create a mobile phone application that uses AI to diagnose tuberculosis and other respiratory diseases.
In a specially contained quiet room, Dr. Videlis Nduba and his team record coughs from people with respiratory diseases like tuberculosis as well as people without disease. The aim is to create software that can differentiate between the two and make a mobile phone application that can accurately recognize a cough connected to TB and other serious diseases.
Natural or forced coughs are collected using three microphones, including a cheap version, a high-definition one and a microphone on a smartphone. The results are sent to the University of Washington which puts them through an existing computer software system called ResNet 18.
Nduba believes that if the software can be proven in trials to perform accurately, it can shorten the time before a patient can get a diagnosis and treatment, and that will help curb the spread of TB.
“The biggest achievement is reduced time to diagnosis. So, from when someone develops TB symptoms, to when a doctor determines they have TB and they need treatment sometimes the average can run from 3 to 2 months to one year. And when they are in the community they are infectious and they are transmitting TB. The moment they get a cough, if you can just expose them to this software and determine this is TB would reduce TB transmission in the community and a lot of TB is due to transmission,” he says.
But the software is not yet accurate enough to meet the standard required by the World Health Organization.
The WHO says the application must be at least 90% accurate in recognizing a TB infection and it must be at least 80% accurate at detecting if no infection exists.
Nduba’s trials so far have shown 80% accuracy at detecting TB and 70% accuracy for detecting there is no TB.
This article was provided by The Associated Press.