AI-supported Microscopy Enhances Detection of Intestinal Parasitic Infections in Primary Healthcare

A new study led by researchers at Uppsala University (UU), Karolinska Institutet (KI), Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Kinondo Kwetu Hospital, Kenya and Muhimbili University of Health and Allied Sciences (MUHAS), Tanzania and demonstrates that artificial intelligence (AI) combined with portable digital microscopy significantly improves the detection of intestinal parasitic worm infections, so called soil-transmitted helminth (STH) infections, in resource-limited settings.

Published in Scientific Reports, the study compares manual microscopy with two AI-based methods - autonomous AI and expert-verified AI - for diagnosing STH in stool samples collected from school children in Kwale County, Kenya. STH, which include roundworm (Ascaris lumbricoides), whipworm (Trichuris trichiura), and hookworm, are among the most prevalent neglected tropical diseases, affecting over 600 million people worldwide.

The AI-based method used portable whole-slide scanners and deep learning algorithms to analyze digitized Kato-Katz smears. Among 704 valid samples, the expert-verified AI detected more infections than traditional manual microscopy, particularly for light-intensity infections that are often missed. Sensitivity for detecting hookworm, T. trichiura, and A. lumbricoides reached 92%, 94%, and 100%, respectively, with expert-verified AI, while specificity remained above 97% for all species.

“Our method could provide accurate, fast, and scalable diagnostics at the point of care, particularly important as global STH prevalence declines and more sensitive methods are required for disease monitoring." says senior co-author Dr. Nina Linder, guest professor at UU. “This research shows the potential of combining portable imaging with AI to overcome long-standing diagnostic challenges in global health,” says Dr. Johan Lundin, professor and senior co-author from KI and FIMM.

"Our results show that the combination of AI and human expertise can surpass both manual microscopy and fully autonomous AI systems – especially in detecting low intensity infections that might otherwise be missed." says Andreas Mårtensson, co-author and professor at UU.

The expert-verified AI system allows local experts to confirm AI findings in less than one minute, drastically reducing workload while increasing accuracy. "The fact that the expert-verified AI had the highest sensitivity for all species shows how AI can help find the needle (parasite egg) in the haystack, enhancing human capabilities and diagnostics" says Dr. Joar von Bahr, PhD student affiliated with KI, UU and FIMM and first author of the article. The study was carried out in collaboration with partners in Kenya, Finland, and Tanzania, and was supported by the Erling-Persson Foundation, the Swedish Research Council, Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation and several private foundations in Finland.

The findings mark a significant step forward in using AI to address diagnostic needs for neglected tropical diseases and highlight UU’s and the partner institutes’ leadership in global digital health innovation.

Read the full article: https://doi.org/10.1038/s41598-025-07309-7