Evidence map›Paper›PMID 34986790›Full record

ArticleBMC medical imaging2022

Deep learning-based pancreas volume assessment in individuals with type 1 diabetes.

Raphael Roger, Melissa A Hilmes, Jonathan M Williams, Daniel J Moore, Alvin C Powers, R Cameron Craddock, John Virostko

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.7field-weighted citation impact, top 17% of its field
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

4 citing papers in PubMed, 8 citations in OpenAlex.

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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

7 authors at 4 institutions in 1 country.

Raphael RogerDepartment of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, 1701 Trinity St., Stop C0200, Austin, TX, 78712, USA.
Melissa A HilmesDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
Jonathan M WilliamsDivision of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Daniel J MooreDepartment of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA.
Alvin C PowersDivision of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
R Cameron CraddockDepartment of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, 1701 Trinity St., Stop C0200, Austin, TX, 78712, USA.
John VirostkoDepartment of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, 1701 Trinity St., Stop C0200, Austin, TX, 78712, USA. jack.virostko@austin.utexas.edu.
Vanderbilt University Medical Center · USThe University of Texas at Austin · USLivestrong Foundation · USVA Tennessee Valley Healthcare System · US

Funding

The Vanderbilt Institute for Clinical and Translational Research (VICTR)UL1TR000445 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BERNARD, GORDON RAPHAEL · 2012 to 2016
$41.4M
NIDDK Network Coordinating UnitU24DK097771 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Shuibing Chen, Jeffrey S. Grethe · 2013 to 2026
$20.9M
Deep Learning of Pancreas MRI to Predict Progression of T1D.R03DK129979 · NIDDK · UNIVERSITY OF TEXAS AT AUSTIN · PI VIROSTKO, JOHN MICHAEL · 2021 to 2022
$317k
NCATS NIH HHS UL1 TR000445NIDDK NIH HHS DK-129979NIDDK NIH HHS R03 DK129979NIDDK NIH HHS U24 DK097771
6 · The paper itself

Abstract

Pancreas volume is reduced in individuals with diabetes and in autoantibody positive individuals at high risk for developing type 1 diabetes (T1D). Studies investigating pancreas volume are underway to assess pancreas volume in large clinical databases and studies, but manual pancreas annotation is time-consuming and subjective, preventing extension to large studies and databases. This study develops deep learning for automated pancreas volume measurement in individuals with diabetes. A convolutional neural network was trained using manual pancreas annotation on 160 abdominal magnetic resonance imaging (MRI) scans from individuals with T1D, controls, or a combination thereof. Models trained using each cohort were then tested on scans of 25 individuals with T1D. Deep learning and manual segmentations of the pancreas displayed high overlap (Dice coefficient = 0.81) and excellent correlation of pancreas volume measurements (R

Indexed as

Deep LearningAdolescentAlgorithmsDiabetes Mellitus, Type 1HumansImage Processing, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingMaleOrgan SizePancreasRetrospective StudiesArtificial intelligenceAutomatic segmentationAuto-segmentationMachine learningMRINeural networkSemanticSizeT1D

Identifiers

PMID34986790
PMCPMC8734282
OpenAlexW4205196630

What OpenQuestion holds

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LicenceCC BY
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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.