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Artificial Intelligence (AI) Detects Anthrax

In an effort to combat bioterrorism, scientists in South Korea have trained artificial intelligence to speedily spot anthrax. The new technique is not 100 percent accurate yet, but it’s orders of magnitude faster than our current testing methods. And it could revolutionize how we screen mysterious white powders for the deadly bioweapon.

Researchers at the Korea Advanced Institute of Science and Technology combined a detailed imaging technique called holographic microscopy with artificial intelligence. The algorithm they created can analyze images of bacterial spores to identify whether they’re anthrax in less than a second. It’s accurate about 96 percent of the time, according to a paper published last week in the journal Science Advances.


Anthrax is an infection caused by the bacteria Bacillus anthracis, which lives in soil. (Both the infection and the bacteria are often referred to as anthrax.) People can accidentally get anthrax infections when they handle the skin or meat of infected animals. But anthrax can also be a dangerous bioweapon: in 2001, anthrax spores sent in the mail infected 22 people and killed five of them.

Once the spores enter the body, they germinate and multiply, causing a flu-like illness that poisons the blood. At least 85 percent of people infected by inhaling the spores die if left untreated, sometimes within just one to two days after symptoms appear. (Anthrax infections of the skin, by contrast, tend to be less fatal.) For people especially at risk of contracting anthrax, like lab workers or people who work with animal hair, there’s a vaccine. For the rest of us, there are antibiotics — but these work best when they’re started as soon as possible after exposure.


So it’s important to detect anthrax fast. Right now, one of the most common methods is to analyze the genetic material of the spores or, once someone is infected, of the bacteria found in infected tissue. But that typically requires giving the spores a little time to multiply in order to yield enough genetic material to analyze. “It’s still going to take the better part of a day with the most rapid approaches to get a result,” says bacteriologist George Stewart at the University of Missouri, who has also developed an anthrax detector and was not involved in this study.

In search of a quicker screening technique, the study’s lead author, physicist YongKeun Park, teamed up with South Korea’s Agency for Defense Development. The goal is to be prepared in case North Korea is developing anthrax as a bioweapon, he says.

Park turned to an imaging technique called holographic microscopy: unlike conventional microscopes, which can only capture the intensity of the light scattering off an object, a holographic microscope can also capture the direction that light is traveling. Since the structure and makeup of a cell can change how light bounces off of it, the researchers suspected that the holographic microscope might capture key, but subtle, differences between spores produced by anthrax and those produced by closely related, but less toxic species.


Park and his team then trained a deep learning algorithm to spot these key differences in more than 400 individual spores from five different species of bacteria. One species was Bacillus anthracis, which causes anthrax, and four were closely related doppelgängers. The researchers didn’t tell the neural network exactly how to spot the different species — the AI figured that out on its own. After some training, it could distinguish the anthrax spores from the non-anthrax doppelgänger species about 96 percent of the time.

The technique isn’t perfect, and as a tool intended to detect bioweapons, it has to be. “The drawback is that the accuracy is lower than conventional methods,” Park says. There are also multiple strains of each of the bacteria species analyzed — but the machine was trained on only one strain per species. Subtle differences between the strains might be able to throw off the algorithm, Stewart says. Still, the new technique is so rapid that it could come in handy. “It doesn’t require culturing organisms, it doesn’t require extracting DNA, it doesn’t require much of anything other than being able to visualize the spores themselves,” Stewart says.


Next, Park wants to feed the neural network more spore images, in order to boost accuracy. In the meantime, the method could be used as a pre-screening tool to rapidly determine whether a white powder that people have been exposed to is anthrax, and if they should start antibiotics. A slower, more accurate method could then confirm the results.

“This paper will not change everything,” Park says, but it’s one step toward a method that can quickly detect anthrax. “It could enhance our preparation for this kind of biological threat.”

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