IsletSwipe, a mobile platform for expert opinion exchange on islet graft images
Jazyk angličtina Země Spojené státy americké Médium print
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
36987915
PubMed Central
PMC10064927
DOI
10.1080/19382014.2023.2189873
Knihovny.cz E-zdroje
- Klíčová slova
- 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,
- MeSH
- Langerhansovy ostrůvky * MeSH
- neuronové sítě MeSH
- pilotní projekty MeSH
- transplantace Langerhansových ostrůvků * metody MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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.
Department of Internal Medicine Leiden University Medical Center Leiden Netheralnds
Diabetes Center Institute for Clinical and Experimental Medicine Prague Czech Republic
Dino School and Novy PORG Prague Czech Republic
Nuffield department of surgical sciences Oxford Consortium for Islet transplantation Oxford UK
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