1. Validation of Vetscan Imagyst ® , a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.
- Author
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Steuer A, Fritzler J, Boggan S, Daniel I, Cowles B, Penn C, Goldstein R, and Lin D
- Subjects
- Animals, Horses parasitology, Ascaridida Infections diagnosis, Ascaridida Infections veterinary, Ascaridida Infections parasitology, Algorithms, Diagnostic Tests, Routine methods, Diagnostic Tests, Routine veterinary, Artificial Intelligence, Strongyle Infections, Equine diagnosis, Strongyle Infections, Equine parasitology, Feces parasitology, Deep Learning, Ascaridoidea isolation & purification, Sensitivity and Specificity, Parasite Egg Count methods, Parasite Egg Count veterinary, Horse Diseases diagnosis, Horse Diseases parasitology
- 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., Methods: The primary objective of the study was to evaluate the performance of the Vetscan Imagyst system in terms of diagnostic sensitivity and specificity in testing equine fecal samples (n = 108) for ova from two parasites that commonly infect horses, strongyles and Parascaris spp., compared to reference assays performed by expert parasitologists using a Mini-FLOTAC technique. Two different fecal flotation solutions were used to prepare the sample slides, NaNO
3 and Sheather's sugar solution., Results: Diagnostic sensitivity of the Vetscan Imagyst algorithm for strongyles versus the manual reference test was 99.2% for samples prepared with NaNO3 solution and 100.0% for samples prepared with Sheather's sugar solution. Sensitivity for Parascaris spp. was 88.9% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Lin's concordance correlation coefficients for VETSCAN IMAGYST eggs per gram counts versus those determined by the expert parasitologist were 0.924-0.978 for strongyles and 0.944-0.955 for Parascaris spp., depending on the flotation solution., Conclusions: Sensitivity and specificity results for detecting strongyles and Parascaris spp. in equine fecal samples showed that Vetscan Imagyst can consistently provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists. As an automated method driven by a deep learning AI algorithm, VETSCAN IMAGYST has the potential to avoid variations in analyst characteristics, thus providing more consistent results in a timely manner, in either clinical or laboratory settings., Competing Interests: Declarations Ethics approval and consent to participate No IACUC or IRB approval was needed for this study design, in accordance with Texas Tech University’s IACUC policies. They were consulted in regard to this study and set up. Consent for publication All authors consent to publication in Parasites and Vectors. Competing interests Ashley Steuer is now employed by Zois, however, at the time of study design and conduct, was under the employ of Texas Tech University., (© 2024. The Author(s).)- Published
- 2024
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