Evidence map›Paper›PMID 39114431›Full record

ArticleJournal of pathology informatics2024

Validation of AI-assisted ThinPrep® Pap test screening using the Genius

Richard L Cantley, Xin Jing, Brian Smola, Wei Hao, Sarah Harrington, Liron Pantanowitz

Erratum issuedAbstract read
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In one paragraph

Article in Journal of pathology informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Review
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  13. The Future of Cervical Cancer Screening.International journal of women's health · 2024
    Review
  14. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Richard L CantleyDepartment of Pathology, University of Michigan-Michigan Medicine, 2800 Plymouth Rd, Building 35, Ann Arbor, MI 48109, USA.
Xin JingDepartment of Pathology, University of Michigan-Michigan Medicine, 2800 Plymouth Rd, Building 35, Ann Arbor, MI 48109, USA.
Brian SmolaDepartment of Pathology, University of Michigan-Michigan Medicine, 2800 Plymouth Rd, Building 35, Ann Arbor, MI 48109, USA.
Wei HaoDepartment of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Sarah HarringtonScientific Affairs, Hologic, Inc., 250 Campus Drive, Marlborough, MA 01752, USA.
Liron PantanowitzDepartment of Pathology, University of Pittsburgh Medical Center, 5230 Centre Avenue, Pittsburgh, PA 15232, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in whole-slide imaging and artificial intelligence present opportunities for improvement in Pap test screening. To date, there have been limited studies published regarding how best to validate newer AI-based digital systems for screening Pap tests in clinical practice. In this study, we validated the Genius™ Digital Diagnostics System (Hologic) by comparing the performance to traditional manual light microscopic diagnosis of ThinPrep

Indexed as

Artificial intelligenceCytologyDigital pathologyPap testScreeningWhole-slide imaging

Identifiers

PMID39114431
PMCPMC11304920

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.