ARUP Sets a New Standard for Ova and Parasite Testing
ARUP is leading the world in the development of advanced technologies to improve detection of gastrointestinal parasites.
August 2019
ARUP becomes the first laboratory to implement an artificial intelligence (AI) screening algorithm to assist in the detection of human gastrointestinal parasites.

February 2021
ARUP implements AI-screening technology to detect coccidia in trichrome-stained and modified acid-fast stained slides.

March 2025
ARUP becomes the first and only lab to implement AI-screening for wet-mount slides.

AI-Augmented Screening Enhances Ova and Parasite Testing
ARUP Laboratories is the first and only laboratory to implement an AI screening tool for the entire ova and parasite laboratory testing process.
For decades, traditional microscopy—a manual and time-consuming process—has remained the standard method for detecting gastrointestinal parasites. Since 2019, ARUP Laboratories has been leading the development and implementation of AI screening to improve detection of these parasites.
Clinical Benefits
By minimizing the inherent potential for human error, AI screening enhances the quality and reliability of tests results. Our validation studies have demonstrated the following benefits:
- Improved sensitivity
- Improved limit of detection
- Improved diagnostic yield
ARUP’s AI-Augmented Gastrointestinal Parasite Testing
- Ova and Parasite Exam, Fecal (Immunocompromised or Travel History), 3001662
- Cryptosporidium and Coccidia Exam, Fecal, 0060046
External Publications:
- Mathison BA, Knight K, Potts J, et al. Detection of protozoan and helminth parasites in concentrated wet mounts of stool using a deep convolutional neural network. J Clin Microbiol. 2025:e0106225.
- Mathison B, Knight KA, Potts JR, et al. Development and validation of an artificial intelligence model for detection of gastrointestinal parasites from concentrated wet-mount stool examinations. Abstract Book 2025: 35th Congress of the European Society of Clinical Microbiology and Infectious Diseases. ESCMID Global; 2025:3361-3362.
- Mathison BA, Kohan JL, Walker JF, et al. Detection of intestinal protozoa in trichrome-stained stool specimens by use of a deep convolutional neural network. J Clin Microbiol. 2020;58(6):e02053-19.
ARUP Resources:
- Journal of Clinical Microbiology Publishes Article on ARUP’s Validation of AI for Parasite Detection
- ‘The Biggest Advancement in Parasite Screening Since the Microscope’: Leveraging Artificial Intelligence Improves Diagnosis
- ARUP Laboratories Expands AI-Augmented Parasitology Screening Tool, Improves Detection and Diagnosis
- ARUP Laboratories Deploys World’s First AI-Augmented Ova and Parasite Assay

















