Evidence map›Paper›PMID 34433345›Full record

ReviewVeterinary pathology2022

Validation of digital microscopy: Review of validation methods and sources of bias.

Christof A Bertram, Nikolas Stathonikos, Taryn A Donovan, Alexander Bartel, Andrea Fuchs-Baumgartinger, Karoline Lipnik, Paul J van Diest, Federico Bonsembiante, Robert Klopfleisch

Abstract readReview
In one paragraph

Review in Veterinary pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Article
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

9 authors.

Christof A BertramUniversity of Veterinary Medicine, Vienna, Austria.ORCID 0000-0002-2402-9997
Nikolas StathonikosUniversity Medical Centre Utrecht, Utrecht, the Netherlands.ORCID 0000-0002-5457-7580
Taryn A DonovanAnimal Medical Center, New York, NY, USA.ORCID 0000-0001-5740-9550
Alexander BartelFreie Universität Berlin, Berlin, Germany.ORCID 0000-0002-1280-6138
Andrea Fuchs-BaumgartingerUniversity of Veterinary Medicine, Vienna, Austria.
Karoline LipnikUniversity of Veterinary Medicine, Vienna, Austria.
Paul J van DiestUniversity Medical Centre Utrecht, Utrecht, the Netherlands.
Federico BonsembianteUniversity of Padova, Legnaro, Padua, Italy.ORCID 0000-0002-2879-7117
Robert KlopfleischFreie Universität Berlin, Berlin, Germany.ORCID 0000-0002-6308-0568

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital microscopy (DM) is increasingly replacing traditional light microscopy (LM) for performing routine diagnostic and research work in human and veterinary pathology. The DM workflow encompasses specimen preparation, whole-slide image acquisition, slide retrieval, and the workstation, each of which has the potential (depending on the technical parameters) to introduce limitations and artifacts into microscopic examination by pathologists. Performing validation studies according to guidelines established in human pathology ensures that the best-practice approaches for patient care are not deteriorated by implementing DM. Whereas current publications on validation studies suggest an overall high reliability of DM, each laboratory is encouraged to perform an individual validation study to ensure that the DM workflow performs as expected in the respective clinical or research environment. With the exception of validation guidelines developed by the College of American Pathologists in 2013 and its update in 2021, there is no current review of the application of methods fundamental to validation. We highlight that there is high methodological variation between published validation studies, each having advantages and limitations. The diagnostic concordance rate between DM and LM is the most relevant outcome measure, which is influenced (regardless of the viewing modality used) by different sources of bias including complexity of the cases examined, diagnostic experience of the study pathologists, and case recall. Here, we review 3 general study designs used for previous publications on DM validation as well as different approaches for avoiding bias.

Indexed as

MicroscopyPathology, VeterinaryAnimalsHumansPathologistsReproducibility of ResultsSpecimen Handlingaccuracyconcordance ratedigital microscopydigital pathologynoninferiorityreviewstudy designvalidationvirtual microscopywhole-slide images

Identifiers

PMID34433345
PMCPMC8761960

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.