Evidence map›Paper›PMID 42384752›Full record

ArticleEmerging microbes & infections2026

Development and validation of the AI-predictive ParaScout

Ihor Feoktistov, Rob Koelewijn, Chris Tieken, Lisette van Lieshout, Serena Slavenburg, Rens Zonneveld, Jaap J van Hellemond

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Emerging microbes & infections, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Ihor FeoktistovLeven Vision, Leiden, The Netherlands.
Rob KoelewijnDepartment of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
Chris TiekenLeven Vision, Leiden, The Netherlands.
Lisette van LieshoutParasitology Research Group, Leiden University Center for Infectious Diseases (LUCID), Leiden University Medical Center (LUMC), Leiden, The Netherlands.
Serena SlavenburgRegional Public Health Laboratory Kennemerland, Haarlem, The Netherlands.
Rens ZonneveldDepartment of Medical Microbiology and Infection Prevention, Amsterdam UMC, Amsterdam, The Netherlands.
Jaap J van HellemondDepartment of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center, Rotterdam, The Netherlands.ORCID 0000-0003-4862-7796

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The comprehensive method to detect gastro-intestinal infections is still microscopic examination of stool specimens. However, manual microscopic examination is laborious, and its accuracy is highly observer-dependent. Therefore, we have developed a deep-learning approach integrated with a digital microscope scanner to detect gastro-intestinal helminths in digital images, which allows faster and more precise detection of gastro-intestinal helminths. A novel

Indexed as

FecesHelminthiasisHelminthsIntestinal Diseases, ParasiticMicroscopyAnimalsDeep LearningHumansIntelligent SystemsSensitivity and SpecificityArtificial intelligence (AI)clinical parasitologydiagnostic microbiologygastro-intestinal helminthsmachine learningmicroscopy

Identifiers

PMID42384752
PMCPMC13366647

What OpenQuestion holds

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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.