In the past, Lenaghan said, scientists would incubate a sample for several days and manually look through a microscope to determine whether Cyclospora oocysts were viable and had sporulated.
Cyclospora is endemic to many parts of the world, including Central and South America. Until 2018, outbreaks in the United States were linked to produce imported from those endemic countries. More recently, a few Cyclospora outbreaks have been tied to U.S.-grown produce.
As part of their initial proof-of-concept trials, Lenaghan said they will use Eimeria oocysts as surrogates for Cyclospora oocysts. Both pathogens are protozoan and have very similar life cycles and similar appearances. But Eimeria oocysts, which are found in poultry droppings, are more readily available than Cyclospora oocysts.
The researchers have enlisted machine learning and artificial intelligence that involves a computerized system to compare microorganisms in a sample to an image library of confirmed pathogens. The more samples that are run, the more the system “learns” how to identify the target organism. The researchers also will use robotics to automate the process.
The first step involved running samples through the high-throughput system to determine how accurate it was in identifying oocysts. Lenaghan said they have reached a 95% confidence level.
Currently, the researchers are working to train the system to differentiate between sporulated and non-sporulated oocysts.
“We have a training set of thousands of images of oocysts,” Lenaghan said. “We manually identify sets that are sporulated or non-sporulated, then train the system.”
Once that is complete, the researchers will run samples of oocysts through the system while a human operator simultaneously scores the results to determine accuracy. For this, they will use Eimeria oocysts obtained from the U.S. Department of Agriculture. Their goal is also to achieve an equally high confidence level of identification.
The second part of their research will involve validating strategies that use gamma radiation, ultra-violet light, ozonation or chlorine dioxide gas to inactivate Cyclospora oocysts.
Finally, they will screen additional compounds, including some chemicals and washes currently used in agricultural systems. The high-throughput system also will allow them to look at different concentrations. Without artificial intelligence and machine learning, Lenaghan said screening this many compounds would likely be impossible.
A human operator would have a difficult time continuously looking through a microscope,” he said, “The advantage of automation and doing machine learning is the instrument can run 24 hours per day."
The research project will also include an economic analysis of the general costs of each treatment.
