Evidence map›Paper›PMID 38645463›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2024

Assessing variability in non-contrast CT for the evaluation of stroke: The effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume.

Spencer H Welland, Grace Hyun J Kim, Anil Yadav, John M Hoffman, William Hsu, Matthew S Brown, Elham Tavakkol, Kambiz Nael, Michael F McNitt-Gray

Open access · greenAbstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 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 39% 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

9 authors at 1 institution in 1 country.

Spencer H WellandCenter for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd. Ste 650, Los Angeles, USA.
Grace Hyun J KimCenter for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd. Ste 650, Los Angeles, USA.
Anil YadavMedical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd. Ste 420, Los Angeles, USA.
John M HoffmanCenter for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd. Ste 650, Los Angeles, USA.
William HsuMedical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd. Ste 420, Los Angeles, USA.
Matthew S BrownCenter for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd. Ste 650, Los Angeles, USA.
Elham TavakkolCenter for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd. Ste 650, Los Angeles, USA.
Kambiz NaelCenter for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd. Ste 650, Los Angeles, USA.
Michael F McNitt-GrayCenter for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, 924 Westwood Blvd. Ste 650, Los Angeles, USA.
University of California, Los Angeles · US

Funding

Computational Toolkit for Normalizing the Impact of CT Acquisition and Reconstruction on Quantitative Image FeaturesR01EB031993 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HSU, WILLIAM, MCNITT-GRAY, MICHAEL F · 2022 to 2024
$2.1M
Computational Toolkit for Normalizing the Impact of CT Acquisition and Reconstruction on Quantitative Image FeaturesR56EB031993 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HSU, WILLIAM, MCNITT-GRAY, MICHAEL F · 2021 to 2021
$612k
NIBIB NIH HHS R01 EB031993
6 · The paper itself

Abstract

Purpose: To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume. The purpose of this work was to investigate the effects of CT reconstruction kernel and slice thickness on ASPECTS and hypodense volume. Methods: The NCCT series image data of 87 patients imaged with a CT stroke protocol at our institution were reconstructed with 3 kernels (H10s-smooth, H40s-medium, H70h-sharp) and 2 slice thicknesses (1.5mm and 5mm) to create a reference condition (H40s/5mm) and 5 non-reference conditions. Each reconstruction for each patient was analyzed with the Brainomix e-Stroke software (Brainomix, Oxford, England) which yields an ASPECTS value and measure of total hypodense volume (mL). Results: An ASPECTS value was returned for 74 of 87 cases in the reference condition (13 failures). ASPECTS in non-reference conditions changed from that measured in the reference condition for 59 cases, 7 of which changed above or below the clinical threshold of 7 for 3 non-reference conditions. ANOVA tests were performed to compare the differences in protocols, Dunnett's post-hoc tests were performed after ANOVA, and a significance level of Conclusion: Automated ASPECTS and hypodense volume measurements can be significantly impacted by reconstruction kernel and slice thickness.

Indexed as

Artificial IntelligenceASPECTSComputer Aided DetectionHypodense VolumeStroke

Identifiers

PMID38645463
PMCPMC11027162
OpenAlexW4393571847

What OpenQuestion holds

Textmetadata
LicenceTDM
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 OpenQuestion graph.