Evidence map›Paper›PMID 34720753›Full record

ArticleProceedings. International Conference on Image Processing2020

A DEEP LEARNING-BASED CAD SYSTEM FOR RENAL ALLOGRAFT ASSESSMENT: DIFFUSION, BOLD, AND CLINICAL BIOMARKERS.

Mohamed Shehata, Mohammed Ghazal, Hadil Abu Khalifeh, Ashraf Khalil, Ahmed Shalaby, Amy C Dwyer, Ashraf M Bakr, Robert Keynton, Ayman El-Baz

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. International Conference on Image Processing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Mohamed ShehataBioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, USA.
Mohammed GhazalFaculty of Engineering, Abu Dhabi University, Abu Dhabi, UAE.
Hadil Abu KhalifehFaculty of Engineering, Abu Dhabi University, Abu Dhabi, UAE.
Ashraf KhalilFaculty of Engineering, Abu Dhabi University, Abu Dhabi, UAE.
Ahmed ShalabyBioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, USA.
Amy C DwyerPediatric Nephrology Unit, Mansoura University Children's Hospital, University of Mansoura, Egypt.
Ashraf M BakrKidney Disease Program, University of Louisville, Louisville, KY, USA.
Robert KeyntonBioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, USA.
Ayman El-BazBioImaging Lab, Bioengineering Department, University of Louisville, Louisville, KY, USA.

Funding

Big data in acute renal rejectionR15AI135924 · NIAID · UNIVERSITY OF LOUISVILLE · PI EL-BAZ, AYMAN S · 2018 to 2018
$455k
NIAID NIH HHS R15 AI135924
6 · The paper itself

Abstract

Recently, studies for non-invasive renal transplant evaluation have been explored to control allograft rejection. In this paper, a computer-aided diagnostic system has been developed to accommodate with an early-stage renal transplant status assessment, called RT-CAD. Our model of this system integrated multiple sources for a more accurate diagnosis: two image-based sources and two clinical-based sources. The image-based sources included apparent diffusion coefficients (ADCs) and the amount of deoxygenated hemoglobin (R2*). More specifically, these ADCs were extracted from 47 diffusion weighted magnetic resonance imaging (DW-MRI) scans at 11 different

Indexed as

BOLD-MRIDW-MRIRT-CADSAEs

Identifiers

PMID34720753
PMCPMC8553095

What OpenQuestion holds

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LicenceTDM
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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.