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Diagnostic performance of McMaster, Wisconsin, and automated egg counting techniques for enumeration of equine strongyle eggs in fecal samples.
Abstract: Fecal egg counts are the cornerstone of equine parasite control programs. Previous work led to the development of an automated, image-analysis-based parasite egg counting system. The system has been further developed to include an automated reagent dispenser unit and a custom camera (CC) unit that generates higher resolution images, as well as a particle shape analysis (PSA) algorithm and machine learning (ML) algorithm. The first aim of this study was to conduct a comprehensive comparison of method precision between the original smartphone (SP) unit with the PSA algorithm, CC/PSA, CC/ML, and the traditional McMaster (MM) and Wisconsin (MW) manual techniques. Additionally, a Bayesian analysis was performed to estimate and compare sensitivity and specificity of all five methods. Feces were collected from horses, screened with triplicate Mini-FLOTAC counts, and placed into five categories: negative (no eggs seen), > 0 - ≤ 200 eggs per gram (EPG), > 200 - ≤ 500 EPG, > 500 - ≤ 1000 EPG, and > 1000 EPG. Ten replicates per horse were analyzed for each technique. Technical variability for samples > 200 EPG was significantly higher for MM than CC/PSA and CC/ML (p 0 was numerically highest for CC/PSA, but with samples > 200 EPG, MM had a significantly lower CV than MW (p = 0.001), MW had a significantly lower CV than CC/PSA (p < 0.0001), CC/ML had a significantly lower CV than both MW and SP/PSA (p 98 % for all five methods with no significant differences. Specificity, however, was significantly the highest for CC/PSA, followed numerically by SP/PSA, MM, CC/ML, and finally MW. Overall, the automated counting system is a promising new development in equine parasitology. Continued refinement to the counting algorithms will help improve precision and specificity, while additional research in areas such as egg loss, analyst variability at the counting step, and accuracy will help create a complete picture of its impact as a new fecal egg count method.
Copyright © 2020 Elsevier B.V. All rights reserved.
Publication Date: 2020-08-07 PubMed ID: 32801106DOI: 10.1016/j.vetpar.2020.109199Google 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.
- Comparative Study
- Journal Article
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 discusses a study that compares traditional manual techniques for counting equine strongyle eggs in fecal samples with an automated, image-analysis-based system. The comparison was focused on precision, sensitivity, and specificity, and the automated system showed promise for improved efficiency and accuracy in egg counting.
Objective of the Study
- The main objective of the study was to evaluate the precision of various egg counting methods, including the traditional McMaster (MM) and Wisconsin (MW) manual techniques, against an automated system using smartphone (SP) units and a custom camera (CC) with machine learning (ML) algorithms and particle shape analysis (PSA).
Methodology
- The researchers collected feces samples from horses, which were then screened and categorized according to their egg count per gram (EPG) using Mini-FLOTAC counts. The categories were: negative (no eggs seen), > 0 – ≤ 200 EPG, > 200 – ≤ 500 EPG, > 500 – ≤ 1000 EPG, and > 1000 EPG.
- Ten replicates per horse for each technique were analyzed.
Findings
- The technical variability for samples > 200 EPG was significantly higher for MM than for the automated techniques (CC/PSA and CC/ML).
- Biological variability for samples > 0 was numerically highest for CC/PSA. However, with samples > 200 EPG, MM displayed significantly lower variability than MW, and MW displayed significantly lower variability than CC/PSA.
- The automated techniques (CC/ML) had a significantly lower variability compared to both MW and SP/PSA.
- Sensitivity was greater than 98 % for all five methods with no significant differences.
- Specificity was highest for the automated technique CC/PSA, followed by SP/PSA, MM, CC/ML, and finally MW.
Conclusions
- The study concludes that the automated egg counting system offers a potentially significant improvement over traditional manual methods in equine parasitology.
- Further refinement of counting algorithms and research into variables like egg loss and accuracy is required to understand fully the impact of automated fecal egg count methods.
Cite This Article
APA
Cain JL, Slusarewicz P, Rutledge MH, McVey MR, Wielgus KM, Zynda HM, Wehling LM, Scare JA, Steuer AE, Nielsen MK.
(2020).
Diagnostic performance of McMaster, Wisconsin, and automated egg counting techniques for enumeration of equine strongyle eggs in fecal samples.
Vet Parasitol, 284, 109199.
https://doi.org/10.1016/j.vetpar.2020.109199 Publication
Researcher Affiliations
- M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA. Electronic address: jennifer.cain@uky.edu.
- M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA; MEP Equine Solutions, 3905 English Oak Circle, Lexington, KY, USA.
- Department of Statistics, University of Kentucky, Lexington, KY, USA.
- M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
- College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN, USA.
- M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
- M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
- M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
- M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
- M.H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
MeSH Terms
- Animals
- Feces / parasitology
- Horses
- Parasite Egg Count / instrumentation
- Parasite Egg Count / standards
- Parasite Egg Count / veterinary
- Sensitivity and Specificity
- Smartphone
- Strongyle Infections, Equine / diagnosis
- Strongyle Infections, Equine / parasitology
Citations
This article has been cited 10 times.- Boelow H, Krücken J, Thomas E, Mirams G, von Samson-Himmelstjerna G. Comparison of FECPAK(G2), a modified Mini-FLOTAC technique and combined sedimentation and flotation for the coproscopic examination of helminth eggs in horses. Parasit Vectors 2022 May 12;15(1):166.
- Ghafar A, Abbas G, King J, Jacobson C, Hughes KJ, El-Hage C, Beasley A, Bauquier J, Wilkes EJA, Hurley J, Cudmore L, Carrigan P, Tennent-Brown B, Nielsen MK, Gauci CG, Beveridge I, Jabbar A. 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.
- Cringoli G, Amadesi A, Maurelli MP, Celano B, Piantadosi G, Bosco A, Ciuca L, Cesarelli M, Bifulco P, Montresor A, Rinaldi L. The Kubic FLOTAC microscope (KFM): a new compact digital microscope for helminth egg counts. Parasitology 2021 Apr;148(4):427-434.
- Siddique A, Batchu P, Shaik A, Gurrapu P, Erukulla TT, Ellington C, Rubio Villa AL, Brown D, Mahapatra A, Panda S, Morgan E, Van Wyk J, Shapiro-Ilan D, Kannan G, Terrill TH. Evaluating the efficacy of bioelectrical impedance analysis using machine learning models for the classification of goats exposed to Haemonchosis. Front Vet Sci 2025;12:1584828.
- Steuer A, Fritzler J, Boggan S, Daniel I, Cowles B, Penn C, Goldstein R, Lin D. 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 2024 Nov 12;17(1):465.
- Capuozzo S, Marrone S, Gravina M, Cringoli G, Rinaldi L, Maurelli MP, Bosco A, Orrù G, Marcialis GL, Ghiani L, Bini S, Saggese A, Vento M, Sansone C. Automating parasite egg detection: insights from the first AI-KFM challenge. Front Artif Intell 2024;7:1325219.
- 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.
- Britton L, Ripley B, Slusarewicz P. Relative egg extraction efficiencies of manual and automated fecal egg count methods in equines. Helminthologia 2024 Mar;61(1):20-29.
- Nielsen MK, Slusarewicz P, Kuzmina TA, Denwood MJ. US-wide equine strongylid egg count data demonstrate seasonal and regional trends. Parasitology 2024 May;151(6):579-586.
- Elghryani N, McAloon C, Mincher C, McOwan T, de Waal T. Comparison of the Automated OvaCyte Telenostic Faecal Analyser versus the McMaster and Mini-FLOTAC Techniques in the Estimation of Helminth Faecal Egg Counts in Equine. Animals (Basel) 2023 Dec 16;13(24).
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