Evidence map›Paper›PMID 33514412›Full record

ArticleParasites & vectors2021

Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm.

Yoko Nagamori, Ruth Hall Sedlak, Andrew DeRosa, Aleah Pullins, Travis Cree, Michael Loenser, Benjamin S Larson, Richard Boyd Smith, Cory Penn, Richard Goldstein

Open access · goldAbstract read
In one paragraph

Article in Parasites & vectors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
4.8field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 33 citations in OpenAlex.

  1. Review
  2. Validation of Vetscan ImagystParasites & vectors · 2024
    Article
  3. Article
  4. Control of companion animal parasites and impact on One Health.One health (Amsterdam, Netherlands) · 2024
    Review
  5. Article
  6. Article
  7. Multicenter evaluation of the Vetscan Imagyst system using Ocus 40 and EasyScan One scanners to detect gastrointestinal parasites in feces of dogs and cats.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2024
    Article
  8. Article
  9. Article
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  11. Article
  12. Review
  13. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Yoko NagamoriDepartment of Veterinary Pathobiology, College of Veterinary Medicine, Oklahoma State University, Stillwater, OK, 74078, USA. yokon@okstate.edu.ORCID http://orcid.org/0000-0002-9515-3215
Ruth Hall SedlakVeterinary Medicine Research and Development, Zoetis, 333 Portage Street, Kalamazoo, MI, 49007, USA.
Andrew DeRosaVeterinary Medicine Research and Development, Zoetis, 333 Portage Street, Kalamazoo, MI, 49007, USA.
Aleah PullinsVeterinary Medicine Research and Development, Zoetis, 333 Portage Street, Kalamazoo, MI, 49007, USA.
Travis CreeVeterinary Medicine Research and Development, Zoetis, 333 Portage Street, Kalamazoo, MI, 49007, USA.
Michael LoenserGlobal Diagnostics, Zoetis, 10 Sylvan Way, Parsippany, NJ, 07054, USA.
Benjamin S LarsonTechcyte, Incorporated, 384 S 400 W #125, Lindon, UT, 84042, USA.
Richard Boyd SmithTechcyte, Incorporated, 384 S 400 W #125, Lindon, UT, 84042, USA.
Cory PennGlobal Diagnostics, Zoetis, 10 Sylvan Way, Parsippany, NJ, 07054, USA.
Richard GoldsteinGlobal Diagnostics, Zoetis, 10 Sylvan Way, Parsippany, NJ, 07054, USA.
Zoetis (United States) · USOklahoma State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFecal examinations in pet cats and dogs are key components of routine veterinary practice; however, their accuracy is influenced by diagnostic methodologies and the experience level of personnel performing the tests. The VETSCAN IMAGYST system was developed to provide simpler and easier fecal examinations which are less influenced by examiners' skills. This system consists of three components: a sample preparation device, an automated microscope scanner, and analysis software. The objectives of this study were to qualitatively evaluate the performance of the VETSCAN IMAGYST system on feline parasites (Ancylostoma and Toxocara cati) and protozoan parasites (Cystoisospora and Giardia) and to assess and compare the performance of the VETSCAN IMAGYST centrifugal flotation method to reference centrifugal and passive flotation methods.

methodsTo evaluate the diagnostic performance of the scanning and algorithmic components of the VETSCAN IMAGYST system, fecal slides were prepared by the VETSCAN IMAGYST centrifugal flotation technique with pre-screened fecal samples collected from dogs and cats and examined by both an algorithm and parasitologists. To assess the performance of the VETSCAN IMAGYST centrifugal flotation technique, diagnostic sensitivity and specificity were calculated and compared to those of conventional flotation techniques.

resultsThe performance of the VETSCAN IMAGYST algorithm closely correlated with evaluations by parasitologists, with sensitivity of 75.8-100% and specificity of 93.1-100% across the targeted parasites. For samples with 50 eggs or less per slide, Lin's concordance correlation coefficients ranged from 0.70 to 0.95 across the targeted parasites. The results of the VETSCAN IMAGYST centrifugal flotation method correlated well with those of the conventional centrifugal flotation method across the targeted parasites: sensitivity of 65.7-100% and specificity of 97.6-100%. Similar results were observed for the conventional passive flotation method compared to the conventional centrifugal flotation method: sensitivity of 56.4-91.7% and specificity of 99.4-100%.

conclusionsThe VETSCAN IMAGYST scanning and algorithmic systems with the VETSCAN IMAGYST fecal preparation technique demonstrated a similar qualitative performance to the parasitologists' examinations with conventional fecal flotation techniques. Given the deep learning nature of the VETSCAN IMAGYST system, its performance is expected to improve over time, enabling it to be utilized in veterinary clinics to perform fecal examinations accurately and efficiently.

Indexed as

Deep LearningAlgorithmsAncylostomaAnimalsCat DiseasesCatsCentrifugationDiagnostic Tests, RoutineDog DiseasesDogsFecesGiardiaHospitals, AnimalOocystsParasite Egg CountParasitesAncylostomaCystCystoisosporaDeep learningFecal egg identificationGiardiaOocystToxocara catiVeterinary parasitology diagnostic

Identifiers

PMID33514412
PMCPMC7844936
OpenAlexW3124733938

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.