IsletSwipe, a mobile platform for expert opinion exchange on islet graft images.
Consensus building
deep learning
expert opinion exchange
ground truth
human islets
image annotation
islet counting
islet graft quality control
islet isolation
islet transplantation
mobile application
user experience
Journal
Islets
ISSN: 1938-2022
Titre abrégé: Islets
Pays: United States
ID NLM: 101495366
Informations de publication
Date de publication:
31 12 2023
31 12 2023
Historique:
medline:
30
3
2023
entrez:
29
3
2023
pubmed:
30
3
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 36987915
doi: 10.1080/19382014.2023.2189873
pmc: PMC10064927
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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