Evidence map›Paper›PMID 35511469›Full record

ReviewThe Journal of pathology2022

High-throughput whole-slide scanning to enable large-scale data repository building.

Mark D Zarella, Keysabelis Rivera Alvarez

Abstract readReview
In one paragraph

Review in The Journal of pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Study on the Transformation Process of Thyroid Fine-Needle Aspiration Liquid-Based Cytology to Whole-Slide Image.Cytopathology : official journal of the British Society for Clinical Cytology · 2025
    Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. 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

2 authors.

Mark D ZarellaDepartment of Pathology, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0002-1722-6773
Keysabelis Rivera AlvarezDepartment of Pathology, Johns Hopkins University, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a necessary first step. AI development benefits from large sample sizes and diverse cohorts, and therefore efforts to digitize glass slides must meet these needs in an efficient and cost-effective manner. Technical innovation in whole-slide imaging has enabled high-throughput slide scanning through the coordinated increase in scanner capacity, speed, and automation. Combining these hardware innovations with automated informatics approaches has enabled more efficient workflows and the opportunity to provide higher-quality imaging data using fewer personnel. Here we review several practical considerations for deploying high-throughput scanning and we present strategies to increase efficiency with a focus on quality. Finally, we review remaining challenges and issue a call to vendors to innovate in the areas of automation and quality control in order to make high-throughput scanning realizable to laboratories with limited resources. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Indexed as

Artificial IntelligenceMicroscopyHumansUnited KingdomWorkflowartificial intelligencecomputational pathologydata repositorydigital pathologymachine learningvirtual slidewhole-slide imagingwhole-slide scanning

Identifiers

PMID35511469
PMCPMC9327504

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.