The effect of analyst training on fecal egg counting variability

Jennifer L. Cain, Kerri T. Peters, Parul Suri, Amber Roher, Matthew H. Rutledge, Martin K. Nielsen

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Fecal egg counts (FECs) are essential for veterinary parasite control programs. Recent advances led to the creation of an automated FEC system that performs with increased precision and reduces the need for training of analysts. However, the variability contributed by analysts has not been quantified for FEC methods, nor has the impact of training on analyst performance been quantified. In this study, three untrained analysts performed FECs on the same slides using the modified McMaster (MM), modified Wisconsin (MW), and the automated system with two different algorithms: particle shape analysis (PSA) and machine learning (ML). Samples were screened and separated into negative (no strongylid eggs seen), 1–200 eggs per gram of feces (EPG), 201–500 EPG, 501–1000 EPG, and 1001+ EPG levels, and ten repeated counts were performed for each level and method. Analysts were then formally trained and repeated the study protocol. Between analyst variability (BV), analyst precision (AP), and the proportion of variance contributed by analysts were calculated. Total BV was significantly lower for MM post-training (p = 0.0105). Additionally, AP variability and analyst variance both tended to decrease for the manual MM and MW methods. Overall, MM had the lowest BV both pre- and post-training, although PSA and ML were minimally affected by analyst training. This research illustrates not only how the automated methods could be useful when formal training is unavailable but also how impactful formal training is for traditional manual FEC methods.

Original languageEnglish
Pages (from-to)1363-1370
Number of pages8
JournalParasitology Research
Volume120
Issue number4
DOIs
StatePublished - Apr 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

Keywords

  • Analyst
  • Automated
  • Fecal egg count
  • Horse
  • McMaster
  • Wisconsin

ASJC Scopus subject areas

  • Parasitology
  • General Veterinary
  • Insect Science
  • Infectious Diseases

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