Evidence map›Paper›PMID 37987817›Full record

ReviewPathologie (Heidelberg, Germany)2023

Digital image analysis and artificial intelligence in pathology diagnostics-the Swiss view.

Sabina Berezowska, Gieri Cathomas, Rainer Grobholz, Maurice Henkel, Wolfram Jochum, Viktor H Koelzer, Mario Kreutzfeldt, Kirsten D Mertz, Matthias Rössle, Davide Soldini and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Pathologie (Heidelberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Sabina BerezowskaInstitut Universitaire de Pathologie, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 25, 1011, Lausanne, Switzerland. sabina.berezowska@chuv.ch.ORCID http://orcid.org/0000-0001-5442-9791
Gieri CathomasInstitute of Tissue Medicine and Pathology, University of Bern, Murtenstr. 31, 3008, Bern, Switzerland.
Rainer GrobholzMedical Faculty, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-1901-7967
Maurice HenkelResearch & Analytic Services, University Hospital Basel, Basel, Switzerland.
Wolfram JochumInstitute of Pathology, Cantonal Hospital St. Gallen, 9007, St. Gallen, Switzerland.
Viktor H KoelzerDepartment of Pathology and Molecular Pathology, University and University Hospital of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-9206-4885
Mario KreutzfeldtDepartment of Pathology and Immunology, University of Geneva, Geneva, Switzerland.
Kirsten D MertzInstitute of Pathology, Cantonal Hospital Baselland, Liestal, Switzerland.ORCID http://orcid.org/0000-0002-3074-6925
Matthias RösslePathologie, Luzerner Kantonsspital, Spitalstr., 6000, Luzern 16, Switzerland.
Davide SoldiniPathologie Zentrum Zürich medica, Hottingerstr. 9, 8024, Zürich, Switzerland.
Inti ZlobecInstitute of Tissue Medicine and Pathology, University of Bern, Murtenstr. 31, 3008, Bern, Switzerland.
Andrew JanowczykDepartment of Biomedical Engineering, Emory University, Atlanta, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital pathology (DP) is increasingly entering routine clinical pathology diagnostics. As digitization of the routine caseload advances, implementation of digital image analysis algorithms and artificial intelligence tools becomes not only attainable, but also desirable in daily sign out. The Swiss Digital Pathology Consortium (SDiPath) has initiated a Delphi process to generate best-practice recommendations for various phases of the process of digitization in pathology for the local Swiss environment, encompassing the following four topics: i) scanners, quality assurance, and validation of scans; ii) integration of scanners and systems into the pathology laboratory information system; iii) the digital workflow; and iv) digital image analysis (DIA)/artificial intelligence (AI). The current article focuses on the DIA-/AI-related recommendations generated and agreed upon by the working group and further verified by the Delphi process among the members of SDiPath. Importantly, they include the view and the currently perceived needs of practicing pathologists from multiple academic and cantonal hospitals as well as private practices.

Indexed as

Artificial IntelligencePathology, ClinicalAlgorithmsDiagnostic ImagingHumansSwitzerlandArtificial intelligenceDelphi processDigitalizationImage analysisPathology

Identifiers

PMID37987817
PMCPMC10739393

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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.