Evidence map›Paper›PMID 34350404›Full record

ArticleRadiology. Artificial intelligence2021

Optimizing Deep Learning Algorithms for Segmentation of Acute Infarcts on Non-Contrast Material-enhanced CT Scans of the Brain Using Simulated Lesions.

Søren Christensen, Michael Mlynash, Julian MacLaren, Christian Federau, Gregory W Albers, Maarten G Lansberg

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

Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 40% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
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

6 authors at 1 institution in 1 country.

Søren ChristensenStanford Stroke Center, Department of Neurology, Stanford University, 780 Welch Rd, Suite 350, Palo Alto, CA 94304 (S.C., M.M., J.M., G.W.A., M.G.L.); and Institute for Biomedical Engineering, ETH Zürich und University of Zürich, Zürich, Switzerland (C.F.).ORCID https://orcid.org/0000-0003-1242-3724
Michael MlynashStanford Stroke Center, Department of Neurology, Stanford University, 780 Welch Rd, Suite 350, Palo Alto, CA 94304 (S.C., M.M., J.M., G.W.A., M.G.L.); and Institute for Biomedical Engineering, ETH Zürich und University of Zürich, Zürich, Switzerland (C.F.).ORCID https://orcid.org/0000-0001-7483-8699
Julian MacLarenStanford Stroke Center, Department of Neurology, Stanford University, 780 Welch Rd, Suite 350, Palo Alto, CA 94304 (S.C., M.M., J.M., G.W.A., M.G.L.); and Institute for Biomedical Engineering, ETH Zürich und University of Zürich, Zürich, Switzerland (C.F.).ORCID https://orcid.org/0000-0001-7073-2926
Christian FederauStanford Stroke Center, Department of Neurology, Stanford University, 780 Welch Rd, Suite 350, Palo Alto, CA 94304 (S.C., M.M., J.M., G.W.A., M.G.L.); and Institute for Biomedical Engineering, ETH Zürich und University of Zürich, Zürich, Switzerland (C.F.).ORCID https://orcid.org/0000-0002-3803-6602
Gregory W AlbersStanford Stroke Center, Department of Neurology, Stanford University, 780 Welch Rd, Suite 350, Palo Alto, CA 94304 (S.C., M.M., J.M., G.W.A., M.G.L.); and Institute for Biomedical Engineering, ETH Zürich und University of Zürich, Zürich, Switzerland (C.F.).ORCID https://orcid.org/0000-0003-0263-4632
Maarten G LansbergStanford Stroke Center, Department of Neurology, Stanford University, 780 Welch Rd, Suite 350, Palo Alto, CA 94304 (S.C., M.M., J.M., G.W.A., M.G.L.); and Institute for Biomedical Engineering, ETH Zürich und University of Zürich, Zürich, Switzerland (C.F.).ORCID https://orcid.org/0000-0002-3545-6927
Institute for Biomedical Engineering · CH

Funding

CTP to predict Response to recanalization in Ischemic Stroke Project (CRISP)R01NS075209 · NINDS · STANFORD UNIVERSITY · PI LANSBERG, MAARTEN G · 2011 to 2023
$6.0M
NINDS NIH HHS R01 NS075209
6 · The paper itself

Abstract

purposeTo test the efficacy of lesion segmentation using a deep learning algorithm on non-contrast material-enhanced CT (NCCT) images with synthetic lesions resembling acute infarcts. MATERIALS AND

methodsIn this retrospective study, 40 diffusion-weighted imaging (DWI) lesions in patients with acute stroke (median age, 69 years; range, 62-76 years; 17 women; screened between 2011 and 2017) were coregistered to 40 normal NCCT scans (median age, 70 years; range, 55-76 years; 25 women; screened between 2008 and 2011), which produced 640 combinations of DWI-NCCT with and without lesions for training (

resultsDice scores of symmetry-aware U-Nets were 25% higher than those of standard U-Nets (median, 0.49 vs 0.65;

conclusionSymmetry-aware U-Nets offer promise for segmentation of acute stroke lesions on NCCT scans.

Indexed as

AdultsCT-QuantitativeStroke

Identifiers

PMID34350404
PMCPMC8328101
OpenAlexW3160495981

What OpenQuestion 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 OpenQuestion graph.