Evidence map›Paper›PMID 36987915›Full record

ArticleIslets2023

IsletSwipe, a mobile platform for expert opinion exchange on islet graft images.

David Habart, Adam Koza, Ivan Leontovyc, Lucie Kosinova, Zuzana Berkova, Jan Kriz, Klara Zacharovova, Bas Brinkhof, Dirk-Jan Cornelissen, Nicholas Magrane and 5 more

Abstract read
In one paragraph

Article in Islets, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

David HabartLaboratory of Pancreatic Islets, Center of Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic.ORCID 0000-0002-4449-6170
Adam KozaDino School & Novy PORG, Prague, Czech Republic.
Ivan LeontovycLaboratory of Pancreatic Islets, Center of Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic.
Lucie KosinovaLaboratory of Pancreatic Islets, Center of Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic.
Zuzana BerkovaLaboratory of Pancreatic Islets, Center of Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic.
Jan KrizDiabetes Center, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
Klara ZacharovovaLaboratory of Pancreatic Islets, Center of Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic.
Bas BrinkhofDepartment of Internal Medicine, Leiden University Medical Center (LUMC), Leiden, Netheralnds.
Dirk-Jan CornelissenDepartment of Internal Medicine, Leiden University Medical Center (LUMC), Leiden, Netheralnds.
Nicholas MagraneNuffield department of surgical sciences, Oxford Consortium for Islet transplantation, Oxford, UK.
Katerina BittenglovaDiabetes Center, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
Martin CapekLight Microscopy Laboratory, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic.
Jan ValeckaLaboratory of Biomathematics, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic.
Alena HabartovaRedox Photochemistry Lab, Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
František SaudekDiabetes Center, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We previously developed a deep learning-based web service (IsletNet) for an automated counting of isolated pancreatic islets. The neural network training is limited by the absent consensus on the ground truth annotations. Here, we present a platform (IsletSwipe) for an exchange of graphical opinions among experts to facilitate the consensus formation. The platform consists of a web interface and a mobile application. In a small pilot study, we demonstrate the functionalities and the use case scenarios of the platform. Nine experts from three centers validated the drawing tools, tested precision and consistency of the expert contour drawing, and evaluated user experience. Eight experts from two centers proceeded to evaluate additional images to demonstrate the following two use case scenarios. The Validation scenario involves an automated selection of images and islets for the expert scrutiny. It is scalable (more experts, images, and islets may readily be added) and can be applied to independent validation of islet contours from various sources. The Inquiry scenario serves the ground truth generating expert in seeking assistance from peers to achieve consensus on challenging cases during the preparation for IsletNet training. This scenario is limited to a small number of manually selected images and islets. The experts gained an opportunity to influence IsletNet training and to compare other experts' opinions with their own. The ground truth-generating expert obtained feedback for future IsletNet training. IsletSwipe is a suitable tool for the consensus finding. Experts from additional centers are welcome to participate.

Indexed as

Islets of LangerhansIslets of Langerhans TransplantationNeural Networks, ComputerPilot ProjectsConsensus buildingdeep learningexpert opinion exchangeground truthhuman isletsimage annotationislet countingislet graft quality controlislet isolationislet transplantationmobile applicationuser experience

Identifiers

PMID36987915
PMCPMC10064927

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.