Artificial intelligence identifies 'kissing bugs' that spread Chagas disease

ScienceDaily | 6/20/2019 | Staff
jenny1246 (Posted by) Level 3
Chagas is particularly nasty because most people who have it don't know they've been infected. But according to the Centers for Disease Control and Prevention, some 20 percent to 30 percent of the 8 million people with Chagas worldwide are struck at some later point with heart rhythm abnormalities that can bring on sudden death; dilated hearts that don't pump blood efficiently; or a dilated esophagus or colon.

The disease is caused most often when triatomine bugs -- more commonly known as "kissing bugs" -- bite people and transmit the parasite Trypanosoma cruzi into their bloodstreams. Chagas is most prevalent in rural areas of Mexico, Central America and South America.

Undertaking - KU - Virtual - Vector - Project

A recent undertaking at KU, called the Virtual Vector Project, sought to enable public health officials to identify triatomine that carry Chagas with their smartphones, using a kind of portable photo studio for taking pictures of the bugs.

Now, a graduate student at KU has built on that project with proof-of-concept research showing artificial intelligence can recognize 12 Mexican and 39 Brazilian species of kissing bugs with high accuracy by analyzing ordinary photos -- an advantage for officials looking to cut the spread of Chagas disease.

Ali - Khalighifar - KU - Student - Biodiversity

Ali Khalighifar, a KU doctoral student at the Biodiversity Institute and the Department of Ecology and Evolutionary Biology, headed a team that just published findings in the Journal of Medical Entomology. To identify the kissing bugs from regular photos, Khalighfar and his colleagues worked with open-source, deep-learning software from Google, called TensorFlow that is similar to the technology underpinning Google's reverse image search.

"Because this model is able to understand, based on pixel tones and colors, what is involved in one image, it can take out the information and analyze it in a way the model can understand -- and then you give them other images to test and...
(Excerpt) Read more at: ScienceDaily
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