New research from the University of Kansas shows machine learning is capable of identifying insects that spread the incurable disease called Chagas with high precision, based on ordinary digital photos. The idea is to give public health officials where Chagas is prevalent a new tool to stem the spread of the disease and eventually to offer identification services directly to the general public.
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.
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, 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 it can identify them with a really good identification rate,” Khalighifar said. “That’s without preprocessing — you just start with raw images, which is awesome. That was the goal. Previously, it was impossible to do the same thing as accurately and certainly not without preprocessing the images.”
Khalighifar and his coauthors — Ed Komp, researcher at KU’s Information and Telecommunication Technology Center, Janine M. Ramsey of Mexico’s Instituto Nacional de Salud Publica, Rodrigo Gurgel-Gonçalves of Brazil’s Universidade de Brasília, and A. Townsend Peterson, KU Distinguished Professor of Ecology and Evolutionary Biology and senior curator with the KU Biodiversity Institute — trained their algorithm with 405 images of Mexican triatomine species and 1,584 images of Brazilian triatomine species.
At first, the team was able to achieve, “83.0 and 86.7 percent correct identification rates across all Mexican and Brazilian species, respectively, an improvement over comparable rates from statistical classifiers,” they write. But after adding information about kissing bugs’ geographic distributions into the algorithm, the researchers boosted the accuracy of identification to 95.8 percent for Mexican species and 98.9 percent for Brazilian species.
According to Khalighifar, the algorithm-based technology could allow public health officials and others to identify triatomine species with an unprecedented level of accuracy, to better understand disease vectors on the ground.
“In the future, we’re hoping to develop an application or a web platform of this model that is constantly trained based on the new images, so it’s always being updated, that provides high-quality identifications to any interested user in real time,” he said.
Khalighifar now is applying a similar approach using TensorFlow for instant identification of mosquitoes based on the sounds of their wings and frogs based on their calls.
“I’m working right now on mosquito recordings,” he said. “I’ve shifted from image processing to signal processing of recordings of the wing beats of mosquitoes. We get the recordings of mosquitoes using an ordinary cell phone, and then we convert them from recordings to images of signals. Then we use TensorFlow to identify the mosquito species. The other project that I’m working right now is frogs, with Dr. Rafe Brown at the Biodiversity Institute. And we are designing the same system to identify those species based on the calls given by each species.”
While often artificial intelligence is popularly portrayed as a job-killing threat or even an existential threat to humanity, Khalighifar said his research showed how AI could be a boon to scientists studying biodiversity.
“It’s amazing — AI really is capable of doing everything, for better or for worse,” he said. “There are uses appearing that are scary, like identifying Muslim faces on the street. Imagine, if we can identify frogs or mosquitoes, how easy it might be to identify human voices. So, there are certainly dark sides of AI. But this study shows a positive AI application — we’re trying to use the good side of that technology to promote biodiversity conservation and support public health work.”
The Latest on: Chagas
via Google News
The Latest on: Chagas
- Bayer’s Lampit approved by FDA to treat Chagas diseaseon August 7, 2020 at 7:45 am
The FDA has approved Bayer AG’s Lampit (nifurtimox) to treat Chagas disease in pediatric patients. Lampit, an oral, antiprotozoal medication, is for newborns to patients less than 18 years old, who ...
- FDA Approves Bayer's Lampit For Treatment Of Chagas Disease In Childrenon August 7, 2020 at 7:05 am
Lampit is not approved in the U.S. for use in adults 18 years of age or older. The company said that Lampit, an antiprotozoal medication will be available in a new, dividable tablet that can be split ...
- FDA Approves Bayer's Lampit For Treatment Of Chagas Disease In Childrenon August 7, 2020 at 6:52 am
Chagas disease is caused by the Trypanosoma cruzi parasite and is primarily transmitted to humans via the feces of infected triatomines, insects that are also known as "kissing bugs" where the ...
- U.S. Food and Drug Administration Approves Lampit® (nifurtimox) for the Treatment of Chagas Disease in Childrenon August 7, 2020 at 6:45 am
Chagas is an infectious tropical disease that affects an estimated 300,000 people in the U.S. 4 The disease is endemic throughout much of Latin America, though it is a growing health concern in ...
- U.S. Food and Drug Administration Approves Lampit® (nifurtimox) for the Treatment of Chagas Disease in Childrenon August 7, 2020 at 6:26 am
Only Chagas disease treatment approved in U.S. for use in children from birth to less than 18 years of age1 New, dividable tablet specially formulated to disperse in water to assist in ...
- U.S. Food and Drug Administration Approves Lampit® (nifurtimox) for the Treatment of Chagas Disease in Childrenon August 7, 2020 at 5:05 am
Bayer announced today that the United States Food and Drug Administration (FDA) has approved Lampit® (nifurtimox) for use in pediatric patients (from birth to less than 18 years of age and weighing at ...
- Experts are seeing increasing numbers of kissing bugs in Texason July 31, 2020 at 5:45 pm
Experts say there has been an increase in the kissing bug population in Texas, potentially putting canines and humans at risk for Chagas.
- Kissing bugs, a cute name with not so cute side effects, spiking in Texason July 30, 2020 at 4:04 am
But experts with Texas A&M AgriLife Research say that bite can affect you down the road because kissing bugs are known to spread Chagas disease. Chagas is caused by a parasite in blood-feeding ...
via Bing News