A Collaborative AI Tool for Detecting Parasitic Infections in Stool Samples
An international team of scientists from Sweden and Finland has developed a cutting edge artificial intelligence tool to identify parasitic infections in patients’ stool samples. The study appears in PLOS Neglected Tropical Diseases, a journal dedicated to overlooked illnesses in low resource settings.
Today, the work of finding parasite eggs in stool largely rests with highly trained laboratory technicians. In regions where laboratories are scarce or testing is too costly, many people do not receive the necessary screening, leaving infections undetected and untreated.
Researchers trained a neural network using about 1.3 thousand stool samples collected from Kenyan children. The biomaterials were examined with an electron microscope and then uploaded to a database to train the algorithm. This approach helps the system learn distinctive features of parasite eggs and improves consistency across samples from different settings.
The study concentrated on three major intestinal parasites: hookworms, roundworms, and whipworms. Infections were diagnosed by identifying parasite eggs in the stool, a traditional method that the AI system now supports with digital analysis and rapid screening capabilities.
The AI showed accuracy between 76 percent and 96 percent, depending on the type of egg. Importantly, the neural network produced very few false positives, with errors in the 1 to 2 percent range, which reduces unnecessary treatments and anxiety for patients.
According to the research team, the test results indicate a high level of reliability and potential for practical deployment in diverse healthcare environments. The system is designed to assist clinicians by providing a fast second look at stool samples, potentially expanding access to diagnostic services in underserved regions.
In related developments, medical researchers have highlighted rare but notable cases where parasites have affected eye health in Congo, underscoring the broad impact of parasitic diseases and the importance of accurate screening tools in improving patient outcomes.