Evidence map›Paper›PMID 38598097›Full record

ReviewPathologie (Heidelberg, Germany)2024

[Artificial intelligence in kidney transplant pathology].

Roman David Bülow, Yu-Chia Lan, Kerstin Amann, Peter Boor

Abstract readEnglish AbstractReview
PubMed Publisher
In one paragraph

Review in Pathologie (Heidelberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Roman David BülowInstitut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland.
Yu-Chia LanInstitut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland.
Kerstin AmannAbteilung Nephropathologie, Institut für Pathologie, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Deutschland.
Peter BoorInstitut für Pathologie, Sektion Nephropathologie, Universitätsklinikum RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Deutschland. pboor@ukaachen.de.ORCID http://orcid.org/0000-0001-9921-4284

Funding

Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie STOP-FSGS-01GM2202CDeutsche Forschungsgemeinschaft 322900939; 322900939HORIZON EUROPE European Research Council 101001791Innovationsfond des Gemeinsamen Bundesausschusses 01VSF21048Medizinische Fakultät der RWTH Aachen 148/21
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology.

aimSummarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide a future outlook. MATERIALS AND

methodsLiterature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney". Based on these results and studies cited in the identified literature, a selection was made of studies that have a histopathological focus and use AI to improve kidney transplant diagnostics. RESULTS AND

conclusionMany studies have already made important contributions, particularly to the automation of the quantification of some histopathological lesions in nephropathology. This likely can be extended to automatically quantify all relevant lesions for a kidney transplant, such as Banff lesions. Important limitations and challenges exist in the collection of representative data sets and the updates of Banff classification, making large-scale studies challenging. The already positive study results make future AI support in kidney transplant pathology appear likely.

Indexed as

Artificial IntelligenceKidney TransplantationDeep LearningHumansKidneyComputer assistanceDatenintegrationDeep learningHistomorphometryKidney transplantation

Identifiers

PMID38598097

What OpenQuestion holds

Textmetadata
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.