Validation of Vetscan Imagyst, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.

Journal: Parasites & vectors
PMID:

Abstract

BACKGROUND: Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm. The study describes the use and validation of Vetscan Imagyst in equine parasitology.

Authors

  • Ashley Steuer
    School of Veterinary Medicine, Texas Tech University, 7671 Evans Drive, Amarillo, TX, 79106, USA.
  • Jason Fritzler
    School of Veterinary Medicine, Texas Tech University, 7671 Evans Drive, Amarillo, TX, 79106, USA.
  • SaraBeth Boggan
    School of Veterinary Medicine, Texas Tech University, 7671 Evans Drive, Amarillo, TX, 79106, USA.
  • Ian Daniel
    School of Veterinary Medicine, Texas Tech University, 7671 Evans Drive, Amarillo, TX, 79106, USA.
  • Bobby Cowles
    Zoetis Inc, 10 Sylvan Way, Parsippany, NJ, 07054, USA. bobby.cowles@zoetis.com.
  • Cory Penn
    Global Diagnostics, Zoetis, 10 Sylvan Way, Parsippany, NJ, 07054, USA.
  • Richard Goldstein
    Zoetis, Global Diagnostics, 10 Sylvan Way, Parsippany, NJ, 07054, USA.
  • Dan Lin
    Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang 330031, China.