Evidence map›Paper›PMID 38510543›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2024

Automated segmentation of the left-ventricle from MRI with a fully convolutional network to investigate CTRCD in breast cancer patients.

Julia Kar, Michael V Cohen, Samuel A McQuiston, Teja Poorsala, Christopher M Malozzi

Open access · greenAbstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 41% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 1 institution in 1 country.

Julia KarUniversity of South Alabama, Departments of Mechanical Engineering and Pharmacology, Alabama, United States.ORCID https://orcid.org/0000-0003-1140-4310
Michael V CohenUniversity of South Alabama, Department of Cardiology, College of Medicine, Alabama, United States.
Samuel A McQuistonUniversity of South Alabama, Department of Radiology, Alabama, United States.
Teja PoorsalaUniversity of South Alabama, Departments of Oncology and Hematology, Alabama, United States.
Christopher M MalozziUniversity of South Alabama, Department of Cardiology, College of Medicine, Alabama, United States.
University of South Alabama · US

Funding

Automated MRI-based 3D Contractility (Strain) Analysis for Detecting Subclinical Cardiotoxicity in Breast Cancer Patients Undergoing ChemotherapyR21EB028063 · NIBIB · UNIVERSITY OF SOUTH ALABAMA · PI KAR, JULIA · 2019 to 2021
$554k
NIBIB NIH HHS R21 EB028063
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

artificial intelligencecardiotoxicitychamber quantificationCTRCDdeep-learningdisplacement encoding with stimulated echoes

Identifiers

PMID38510543
PMCPMC10950093
OpenAlexW4392949565

What Socratic holds

Textmetadata
Read underepoch 390

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 Socratic graph.