Detail
Článek
Článek online
FT
Medvik - BMČ
  • Je něco špatně v tomto záznamu ?

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

D. Habart, A. Koza, I. Leontovyc, L. Kosinova, Z. Berkova, J. Kriz, K. Zacharovova, B. Brinkhof, DJ. Cornelissen, N. Magrane, K. Bittenglova, M. Capek, J. Valecka, A. Habartova, F. Saudek

. 2023 ; 15 (1) : 2189873. [pub] 2023Dec31

Jazyk angličtina Země Spojené státy americké

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc23003575

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.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc23003575
003      
CZ-PrNML
005      
20251105105720.0
007      
ta
008      
230418s2023 xxu f 000 0|eng||
009      
AR
024    7_
$a 10.1080/19382014.2023.2189873 $2 doi
035    __
$a (PubMed)36987915
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxu
100    1_
$a Habart, David $u Laboratory of Pancreatic Islets, Center of Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic $1 https://orcid.org/0000000244496170
245    10
$a IsletSwipe, a mobile platform for expert opinion exchange on islet graft images / $c D. Habart, A. Koza, I. Leontovyc, L. Kosinova, Z. Berkova, J. Kriz, K. Zacharovova, B. Brinkhof, DJ. Cornelissen, N. Magrane, K. Bittenglova, M. Capek, J. Valecka, A. Habartova, F. Saudek
520    9_
$a 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.
650    _2
$a znalecký posudek $7 D005104
650    _2
$a pilotní projekty $7 D010865
650    12
$a Langerhansovy ostrůvky $7 D007515
650    12
$a transplantace Langerhansových ostrůvků $x metody $7 D016381
650    _2
$a neuronové sítě $7 D016571
655    _2
$a časopisecké články $7 D016428
700    1_
$a Koza, Adam $u Dino School & Novy PORG, Prague, Czech Republic
700    1_
$a Leontovyc, Ivan $u Laboratory of Pancreatic Islets, Center of Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic
700    1_
$a Kosinova, Lucie $u Laboratory of Pancreatic Islets, Center of Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic
700    1_
$a Berková, Zuzana $u Laboratory of Pancreatic Islets, Center of Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic $7 xx0081890
700    1_
$a Kriz, Jan $u Diabetes Center, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
700    1_
$a Zacharovova, Klara $u Laboratory of Pancreatic Islets, Center of Experimental Medicine, Institute for Clinical and Experimental Medicine (IKEM), Prague, Czech Republic
700    1_
$a Brinkhof, Bas $u Department of Internal Medicine, Leiden University Medical Center (LUMC), Leiden, Netheralnds
700    1_
$a Cornelissen, Dirk-Jan $u Department of Internal Medicine, Leiden University Medical Center (LUMC), Leiden, Netheralnds
700    1_
$a Magrane, Nicholas $u Nuffield department of surgical sciences, Oxford Consortium for Islet transplantation, Oxford, UK
700    1_
$a Bittenglova, Katerina $u Diabetes Center, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
700    1_
$a Capek, Martin $u Light Microscopy Laboratory, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic $u Laboratory of Biomathematics, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
700    1_
$a Valecka, Jan $u Laboratory of Biomathematics, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
700    1_
$a Habartova, Alena $u Redox Photochemistry Lab, Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic
700    1_
$a Saudek, František $u Diabetes Center, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
773    0_
$w MED00205664 $t Islets $x 1938-2022 $g Roč. 15, č. 1 (2023), s. 2189873
856    41
$u https://pubmed.ncbi.nlm.nih.gov/36987915 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y p $z 0
990    __
$a 20230418 $b ABA008
991    __
$a 20251105105718 $b ABA008
999    __
$a ok $b bmc $g 1924332 $s 1189784
BAS    __
$a 3
BAS    __
$a PreBMC-MEDLINE
BMC    __
$a 2023 $b 15 $c 1 $d 2189873 $e 2023Dec31 $i 1938-2022 $m Islets $n Islets $x MED00205664
LZP    __
$a Pubmed-20230418
Zpět

Najít záznam

Citační ukazatele

Pouze přihlášení uživatelé

Možnosti archivace