Development and performance of an automated fecal egg count system for small ruminant strongylids.
Abstract: An automated equine fecal egg count test, known as the Parasight System, was modified for use with small ruminants. Modifications included the introduction of a short centrifugation step in a floatation medium, an adjustment in pre-test sample filtering, and training of an image analysis-based egg counting algorithm to recognize and enumerate trichostrongylid eggs. In preliminary assessments, the modified method produced trichostrongylid egg counts comparable to manual McMaster analyses of the same samples from both ovine and caprine sources. The coefficient of determination (R) for the linear correlation between McMaster and automated counts from these samples was 0.958, and there were no significant differences when comparing counts using feces from either sheep or goats. More extensive comparison utilized ovine samples split into three groups based on trichostrongylid egg content: Low (201-500 EPG), Medium (501-1000 EPG) and High (1001 or greater EPG). Each group contained 5 samples, each of which was used to produce individual slurries that were counted 8 times each using both McMaster and the automated method. This, again, showed no difference in accuracy between the techniques, but revealed significantly higher precision, as assessed by coefficients of variation (CoV), for the automated method for determining egg counts in the Low and Medium groups. The CoV of the McMaster method was 2.2, 2.5 and 1.3 times greater than the automated in the Low, Medium and High groups, respectively. Overall, the automated egg counting system showed good linear agreement with trichostrongylid egg counts determined with the McMaster method, and demonstrated significantly better precision. This technology reduces operator error and the results presented here illustrate its utility for determination of small ruminant trichostrongylid fecal egg counts.
Copyright © 2021 Elsevier B.V. All rights reserved.
Publication Date: 2021-05-14 PubMed ID: 34020379DOI: 10.1016/j.vetpar.2021.109442Google Scholar: Lookup
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Summary
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This study describes the modification and testing of an automated system for counting parasite eggs in the feces of small ruminants. The modified counting system demonstrated accuracy comparable to manual methods, but with significantly higher precision.
Introduction and Method Modification
- The research team modified an existing automated fecal egg count system (the Parasight System) previously designed for use with horses to now be applicable to small ruminants like sheep and goats.
- Modifications included introducing a centrifugation stage, changing the initial sample filtering process, and training an image analysis algorithm to identify and count trichostrongylid eggs.
Preliminary Assessments
- The team first tested the modified automated system by comparing its results against those obtained by the manual (McMaster) analysis on the same fecal samples from both sheep and goats.
- The correlation coefficient of 0.958 demonstrated a strong linear relationship between both the manual and automated egg counts, suggesting comparability of the two methods.
- There was no significant difference between the counts obtained from sheep or goat feces, showing the versatility of the modified system.
Detailed Comparisons
- The team then divided sheep fecal samples into three categories based on egg content: Low (201-500 eggs per gram), Medium (501-1000 eggs per gram), and High (1001 or more eggs per gram).
- The experimental design involved making eight egg counts each from five samples in each group, using both the McMaster and automated method.
- Again, no significant difference was observed between the methods’ accuracy. However, the automated method offered substantially improved precision, as indicated by the reduced coefficients of variation.
Conclusion
- Overall, the automated egg counting system, when compared to the McMaster manual counting method, showed both comparable accuracy and improved precision.
- By automating the count, the system was able to minimize errors that might have been generated by the operator in the manual method.
- The results highlight the modified system’s potential for producing reliable and precise small ruminant trichostrongylid fecal egg counts.
Cite This Article
APA
Slusarewicz P, Slusarewicz JH, Nielsen MK.
(2021).
Development and performance of an automated fecal egg count system for small ruminant strongylids.
Vet Parasitol, 295, 109442.
https://doi.org/10.1016/j.vetpar.2021.109442 Publication
Researcher Affiliations
- MEP Equine Solutions, 3905 English Oak Circle, Lexington, KY, 40514, USA; M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, 1400 Nicholasville Road, Lexington, KY, 40546, USA.
- Department of Engineering and Public Policy, Carnegie Mellon University, 5215 Wean Hall, Pittsburgh, PA, 15213, USA.
- M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, 1400 Nicholasville Road, Lexington, KY, 40546, USA. Electronic address: martin.nielsen@uky.edu.
MeSH Terms
- Algorithms
- Animals
- Automation, Laboratory
- Feces / parasitology
- Gastrointestinal Diseases / parasitology
- Gastrointestinal Diseases / veterinary
- Goats / parasitology
- Horses
- Image Processing, Computer-Assisted
- Parasite Egg Count / instrumentation
- Parasite Egg Count / methods
- Ruminants / parasitology
- Sheep / parasitology
- Trichostrongyloidea / physiology
Citations
This article has been cited 3 times.- Hempstead MN, Candy PM, Hannaford R, Ross MA, Sutherland IA, Sauermann CW. Threshold levels of artificial infection with Haemonchus contortus impacting lamb physiology and production. Sci Rep 2025 Jun 4;15(1):19551.
- Castle TG, Britton L, Ripley B, Ubelhor E, Slusarewicz P. Evaluation of Parasight All-in-One system for the automated enumeration of helminth ova in canine and feline feces. Parasit Vectors 2024 Jun 27;17(1):275.
- Xiang H, Fang Y, Tan Z, Zhong R. Haemonchus contortus Infection Alters Gastrointestinal Microbial Community Composition, Protein Digestion and Amino Acid Allocations in Lambs. Front Microbiol 2021;12:797746.
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