Journal Article

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

Daniel, Ian, Boggan, SaraBeth, Lin, Dan, Penn, Cory, Steuer, Ashley, Fritzler, Jason, Goldstein, Richard, Cowles, Bobby

2024

Overview

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 NaNO 3 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 NaNO 3 and Sheather’s sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO 3 and Sheather’s sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO 3 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. Graphical Abstract

• Publisher: BioMed Central,BMC
• Subject: Algorithms / Animals / Artificial Intelligence / Ascaridida Infections - diagnosis / Ascaridida Infections - parasitology / Ascaridida Infections - veterinary / Ascaridoidea - isolation & purification / automation / Biomedical and Life Sciences / Biomedicine / computer software / Deep Learning / Diagnostic / diagnostic sensitivity / diagnostic specificity / diagnostic techniques / Diagnostic Tests, Routine - methods / Diagnostic Tests, Routine - veterinary / eggs / Entomology / Equine / Fecal egg / feces / Feces - parasitology / Horse Diseases - diagnosis / Horse Diseases - parasitology / horses / Horses - parasitology / Infectious Diseases / Parascaris / Parasite Egg Count - methods / Parasite Egg Count - veterinary / parasites / parasitic diseases / Parasitology / pets / Sensitivity and Specificity / Strongyle Infections, Equine - diagnosis / Strongyle Infections, Equine - parasitology / Strongylidae / sugars / Tropical Medicine / Veterinary Medicine/Veterinary Science / Virology