Validation of Vetscan Imagyst®, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.
Abstract: 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 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 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 and Sheather's sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO and Sheather's sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO 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.
© 2024. The Author(s).
Publication Date: 2024-11-12 PubMed ID: 39533358PubMed Central: PMC11558902DOI: 10.1186/s13071-024-06525-wGoogle Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
- Journal Article
- Validation Study
Summary
This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.
The research article presents the testing and validation of a novel diagnostic tool, Vetscan Imagyst, that uses artificial intelligence (AI) for detecting specific parasites in horse fecal samples. The study demonstrates that the AI-driven method effectively reduces the inaccuracies and variability often seen in manual fecal egg counts.
Research Objectives
- The primary goal of the research was to test and validate the performance of the Vetscan Imagyst system, an automated fecal parasite detection system. This system integrates AI to accurately diagnose horse fecal samples for parasites such as strongyles and Parascaris spp.
- The researchers compared the diagnostic sensitivity and specificity of the Vetscan Imagyst system against a conventional diagnostic method involving manual fecal egg counts performed by expert parasitologists.
Research Methodology
- The experiment involved testing 108 equine fecal samples.
- Two different fecal flotation solutions, NaNO and Sheather’s sugar solution, were used to prepare the sample slides for scanning.
- The system digitized the sample slides and uploaded them to the cloud.
- A deep learning AI algorithm interacted with the images on the cloud for diagnostic prediction.
Results of the Study
- The results showed that the Vetscan Imagyst system had an exceptionally high diagnostic sensitivity for both strongyles and Parascaris spp., regardless of the flotation solution used.
- Specificity, that is, the system’s ability to correctly identify negative cases, was also high.
- The results of the Vetscan Imagyst system were strongly concordant with those determined by expert parasitologists. This implies a high degree of accuracy and consistency.
Conclusion and Implications
- Based on the outcomes, the researchers concluded that the Vetscan Imagyst system can provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists.
- The consistent results indicate that this AI-driven method can potentially minimize variability in analyst outcomes and provide more reliable results.
- The speed and convenience of the method make it suitable for use in both clinical and laboratory settings.
Cite This Article
APA
Steuer A, Fritzler J, Boggan S, Daniel I, Cowles B, Penn C, Goldstein R, Lin D.
(2024).
Validation of Vetscan Imagyst®, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples.
Parasit Vectors, 17(1), 465.
https://doi.org/10.1186/s13071-024-06525-w Publication
Researcher Affiliations
- School of Veterinary Medicine, Texas Tech University, 7671 Evans Drive, Amarillo, TX, 79106, USA.
- Zoetis Inc, 10 Sylvan Way, Parsippany, NJ, 07054, USA.
- School of Veterinary Medicine, Texas Tech University, 7671 Evans Drive, Amarillo, TX, 79106, USA.
- School of Veterinary Medicine, Texas Tech University, 7671 Evans Drive, Amarillo, TX, 79106, USA.
- School of Veterinary Medicine, Texas Tech University, 7671 Evans Drive, Amarillo, TX, 79106, USA.
- Zoetis Inc, 10 Sylvan Way, Parsippany, NJ, 07054, USA. bobby.cowles@zoetis.com.
- Zoetis Inc, Veterinary Medicine Research and Development, 333 Portage St, Kalamazoo, MI, 49007, USA.
- Zoetis Inc, Veterinary Medicine Research and Development, 333 Portage St, Kalamazoo, MI, 49007, USA.
- Analitix Giant Clinical Research Co., LTD, Commercial Center Bldg 1, 258 Lvdi Avenue, Huaqiao, Kunshan, Suzhou, 21532, China.
MeSH Terms
- Animals
- Horses / 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
- 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
Conflict of Interest Statement
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. All authors consent to publication in Parasites and Vectors. Ashley Steuer is now employed by Zois, however, at the time of study design and conduct, was under the employ of Texas Tech University.
References
This article includes 27 references
- Boelow H, Krücken J, Thomas E, Mirams G, von Samson-Himmelstjerna G. Comparison of FECPAKG2, a modified Mini-FLOTAC technique and combined sedimentation and flotation for the coproscopic examination of helminth eggs in horses.. Parasit Vectors 2022;15:166.
- Ghafar A, Abbas G, King J, Jacobson C, Hughes KJ, El-Hage C. Comparative studies on faecal egg counting techniques used for the detection of gastrointestinal parasites of equines: a systematic review.. Curr Res Parasitol Vector Borne Dis 2021;1:100046.
- Tyson F, Dalesman S, Brophy PM, Morphew RM. Novel equine faecal egg diagnostics: validation of the FECPAKG2.. Animals (Basel) 2020;10:1254.
- Ballweber LR, Beugnet F, Marchiondo AA, Payne PA. American association of veterinary parasitologists’ review of veterinary fecal flotation methods and factors influencing their accuracy and use—is there really one best technique?. Vet Parasitol 2014;204:73–80.
- Cain JL, Slusarewicz P, Rutledge MH, McVey MR, Wielgus KM, Zynda HM. Diagnostic performance of McMaster, Wisconsin, and automated egg counting techniques for enumeration of equine strongyle eggs in fecal samples.. Vet Parasitol 2020;284:109199.
- Nagamori Y, Hall Sedlak R, DeRosa A, Pullins A, Cree T, Loenser M. Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm.. Parasit Vectors 2020;13:346.
- Barda B, Cajal P, Villagran E, Cimino R, Juarez M, Krolewiecki A. Mini-FLOTAC, Kato-Katz and McMaster: three methods, one goal; highlights from north Argentina.. Parasit Vectors 2014;7:271.
- Gates MC, Nolan TJ. Comparison of passive fecal flotation run by veterinary students to zinc-sulfate centrifugation flotation run in a diagnostic parasitology laboratory.. J Parasitol 2009;95:1213–4.
- Alva A, Cangalaya C, Quiliano M, Krebs C, Gilman RH, Sheen P. Mathematical algorithm for the automatic recognition of intestinal parasites.. PLoS ONE 2017;12:e0175646.
- Inácio SV, Ferreira Gomes J, Xavier Falcão A, Nagase Suzuki CT, Bertequini Nagata W, Nery Loiola SH. Automated diagnosis of canine gastrointestinal parasites using image analysis.. Pathogens 2020;9:139.
- Naing KM, Boonsang S, Chuwongin S, Kittichai V, Tongloy T, Prommongkol S. Automatic recognition of parasitic products in stool examination using object detection approach.. PeerJ Comput Sci 2022;8:e1065.
- Rhoads DD. Computer Vision and artificial intelligence are emerging diagnostic tools for the clinical microbiologist.. J Clin Microbiol 2020;58:e00511-e520.
- Rinaldi L, Krücken J, Martinez-Valladares M, Pepe P, Maurelli MP, de Queiroz C. Advances in diagnosis of gastrointestinal nematodes in livestock and companion animals.. Adv Parasitol 2022;118:85–176.
- Scare JA, Slusarewicz P, Noel ML, Wielgus KM, Nielsen MK. Evaluation of accuracy and precision of a smartphone based automated parasite egg counting system in comparison to the McMaster and Mini-FLOTAC methods.. Vet Parasitol 2017;247:85–92.
- Ward P, Dahlberg P, Lagatie O, Larsson J, Tynong A, Vlaminck J. Affordable artificial intelligence-based digital pathology for neglected tropical diseases: a proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears.. PLoS Negl Trop Dis 2022;16:e0010500.
- Herman DS, Rhoads DD, Schulz WL, Durant TJS. Artificial intelligence and mapping a new direction in laboratory medicine: A review.. Clin Chem 2021;67:1466–82.
- Smith KP, Kirby JE. Image analysis and artificial intelligence in infectious disease diagnostics.. Clin Microbiol Infect 2020;26:1318–23.
- Nagamori Y, Sedlak RH, DeRosa A, Pullins A, Cree T, Loenser M. Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm.. Parasit Vectors 2021;14:89.
- Bellaw JL, Nielsen MK. Meta-analysis of cyathostomin species-specific prevalence and relative abundance in domestic horses from 1975–2020: emphasis on geographical region and specimen collection method.. Parasit Vectors 2020;13:509.
- Cain JL, Nielsen MK. The equine ascarids: resuscitating historic model organisms for modern purposes.. Parasitol Res 2022;121:2775–91.
- Corning S. Equine cyathostomins: a review of biology, clinical significance and therapy.. Parasit Vectors 2009;2:1.
- Steuer AE, Anderson HP, Shepherd T, Clark M, Scare JA, Gravatte HS. Parasite dynamics in untreated horses through one calendar year.. Parasit Vectors 2022;15:50.
- Cringoli G, Maurelli MP, Levecke B, Bosco A, Vercruysse J, Utzinger J. The Mini-FLOTAC technique for the diagnosis of helminth and protozoan infections in humans and animals.. Nat Protoc 2017;12:1723–32.
- Zajac AM, Conboy GA, Little SE, Reichard MV. Fecal examination for the diagnosis of parasitism.. Veterinary clinical parasitology 2012;p. 1–182.
- Pereckiene A, Kaziūnaite V, Vysniauskas A, Petkevicius S, Malakauskas A, Sarkūnas M. A comparison of modifications of the McMaster method for the enumeration of Ascarissuum eggs in pig faecal samples.. Vet Parasitol 2007;149:111–6.
- Silva P, Luz A, Rolim A, Carvalho R, Gomes L, Neto I. Comparison study between four coprological methods and two flotation solutions in Sorraia horse (Equus ferus caballus)—Preliminary results.. 2019.
- Nielsen MK, Branan MA, Wiedenheft AM, Digianantonio R, Garber LP, Kopral CA. Parasite control strategies used by equine owners in the United States: a national survey.. Vet Parasitol 2018;250:45–51.
Citations
This article has been cited 0 times.Use Nutrition Calculator
Check if your horse's diet meets their nutrition requirements with our easy-to-use tool Check your horse's diet with our easy-to-use tool
Talk to a Nutritionist
Discuss your horse's feeding plan with our experts over a free phone consultation Discuss your horse's diet over a phone consultation
Submit Diet Evaluation
Get a customized feeding plan for your horse formulated by our equine nutritionists Get a custom feeding plan formulated by our nutritionists
