Evidence map›Paper›PMID 38732280›Full record

ArticleDiagnostics (Basel, Switzerland)2024

Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.

Nicola Fink, Basel Yacoub, U Joseph Schoepf, Emese Zsarnoczay, Daniel Pinos, Milan Vecsey-Nagy, Saikiran Rapaka, Puneet Sharma, Jim O'Doherty, Jens Ricke and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2024. 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
–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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. 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

12 authors.

Nicola FinkDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA.ORCID 0000-0002-3089-9606
Basel YacoubDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA.
U Joseph SchoepfDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA.
Emese ZsarnoczayDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA.
Daniel PinosDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA.ORCID 0000-0002-1697-477X
Milan Vecsey-NagyDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA.
Saikiran RapakaSiemens Healthineers, Princeton, NJ 08540, USA.ORCID 0000-0001-6527-0974
Puneet SharmaSiemens Healthineers, Princeton, NJ 08540, USA.
Jim O'DohertySiemens Medical Solutions, Malvern, PA 19355, USA.ORCID 0000-0003-2989-4485
Jens RickeDepartment of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany.
Akos Varga-SzemesDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA.ORCID 0000-0002-2781-7462
Tilman EmrichDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA.ORCID 0000-0003-4156-7727

Funding

Siemens Healthineers
6 · The paper itself

Abstract

This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly (

Indexed as

aortic aneurysmaortic dissectionartificial intelligencecomputed tomographydeep learning

Identifiers

PMID38732280
PMCPMC11083497

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Registered trials

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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.