Evidence map›Paper›PMID 28877052›Full record

ArticleThe American journal of surgical pathology2018

Diagnostic Efficiency in Digital Pathology: A Comparison of Optical Versus Digital Assessment in 510 Surgical Pathology Cases.

Anne M Mills, Sarah E Gradecki, Bethany J Horton, Rebecca Blackwell, Christopher A Moskaluk, James W Mandell, Stacey E Mills, Helen P Cathro

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in The American journal of surgical pathology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

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

  1. Pooled it
  2. Trial
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  6. Review
  7. Article
  8. Assessment of deep learning assistance for the pathological diagnosis of gastric cancer.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022
    Article
  9. AI Model for Prostate Biopsies Predicts Cancer Survival.Diagnostics (Basel, Switzerland) · 2022
    Article
  10. Integrating digital pathology into clinical practice.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022
    Review
  11. Measuring Digital Pathology Throughput and Tissue Dropouts.Journal of pathology informatics · 2022
    Article
  12. Review
  13. Computational healthcare: Present and future perspectives (Review).Experimental and therapeutic medicine · 2021
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Anne M MillsDepartment of Pathology.
Sarah E GradeckiDepartment of Pathology.
Bethany J HortonDepartment of Public Health Sciences, University of Virginia, Charlottesville, VA.
Rebecca BlackwellDepartment of Pathology.
Christopher A MoskalukDepartment of Pathology.
James W MandellDepartment of Pathology.
Stacey E MillsDepartment of Pathology.
Helen P CathroDepartment of Pathology.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prior work has shown that digital images and microscopic slides can be interpreted with comparable diagnostic accuracy. Although accuracy has been well-validated, the interpretative time for digital images has scarcely been studied and concerns about efficiency remain a major barrier to adoption. We investigated the efficiency of digital pathology when compared with glass slide interpretation in the diagnosis of surgical pathology biopsy and resection specimens. Slides were pulled from 510 surgical pathology cases from 5 organ systems (gastrointestinal, gynecologic, liver, bladder, and brain). Original diagnoses were independently confirmed by 2 validating pathologists. Diagnostic slides were scanned using the Philips IntelliSite Pathology Solution. Each case was assessed independently on digital and optical by 3 reading pathologists, with a ≥6 week washout period between modalities. Reading pathologists recorded assessment times for each modality; digital times included time to load the case. Diagnostic accuracy was determined based on whether a rendered diagnosis differed significantly from the original diagnosis. Statistical analysis was performed to assess for differences in interpretative times across modalities. All 3 reading pathologists showed comparable diagnostic accuracy across optical and digital modalities (mean major discordance rates with original diagnosis: 4.8% vs. 4.4%, respectively). Mean assessment times ranged from 1.2 to 9.1 seconds slower on digital versus optical. The slowest reader showed a significant learning effect during the course of the study so that digital assessment times decreased over time and were comparable with optical times by the end of the series. Organ site and specimen type did not significantly influence differences in interpretative times. In summary, digital image reading times compare favorably relative to glass slides across a variety of organ systems and specimen types. Mean increase in assessment time is 4 seconds/case. This time can be minimized with experience and may be further balanced by the improved ease of electronic chart access allowed by digital slide viewing, as well as quantitative assessments which can be expedited on digital images.

Indexed as

EfficiencyImage Processing, Computer-AssistedHistocytological Preparation TechniquesHumansLinear ModelsObserver VariationPathology, SurgicalReproducibility of ResultsTime Factors

Identifiers

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Registered trials

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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.