Evidence map›Paper›PMID 33934065›Full record

ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2021

Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time.

Haoyue Zhang, Jennifer S Polson, Kambiz Nael, Noriko Salamon, Bryan Yoo, Suzie El-Saden, Fabien Scalzo, William Speier, Corey W Arnold

Abstract read
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In one paragraph

Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  15. A Machine Learning Approach to Predict Acute Ischemic Stroke Thrombectomy Reperfusion using Discriminative MR Image Features.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2021
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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.

Haoyue ZhangComputational Diagnostics Lab, University of California, Los Angeles, CA 90024, USA; Department of Bioengineering, University of California, Los Angeles, CA 90024, USA.
Jennifer S PolsonComputational Diagnostics Lab, University of California, Los Angeles, CA 90024, USA; Department of Bioengineering, University of California, Los Angeles, CA 90024, USA.
Kambiz NaelDepartment of Radiology, University of California, Los Angeles, CA 90024, USA.
Noriko SalamonDepartment of Radiology, University of California, Los Angeles, CA 90024, USA.
Bryan YooDepartment of Radiology, University of California, Los Angeles, CA 90024, USA.
Suzie El-SadenDepartment of Radiology, VA Phoenix Healthcare system, AZ 85012, USA.
Fabien ScalzoDepartments of Neurology and Computer Science, University of California, Los Angeles, CA 90024, USA.
William SpeierComputational Diagnostics Lab, University of California, Los Angeles, CA 90024, USA; Department of Radiology, University of California, Los Angeles, CA 90024, USA.
Corey W ArnoldComputational Diagnostics Lab, University of California, Los Angeles, CA 90024, USA; Department of Bioengineering, University of California, Los Angeles, CA 90024, USA; Department of Radiology, University of California, Los Angeles, CA 90024, USA; Department of Pathology, University of California, Los Angeles, CA 90024, USA.

Funding

Medical Imaging Informatics Training GrantT32EB016640 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ALEX BUI, William Hsu · 2013 to 2026
$2.3M
A Machine Learning Approach to Classifying Time Since Stroke using Medical ImagingR01NS100806 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARNOLD, COREY WELLS · 2018 to 2022
$2.2M
NIBIB NIH HHS T32 EB016640NINDS NIH HHS R01 NS100806
6 · The paper itself

Abstract

Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in up to 25% of patients with unwitnessed AIS. Current clinical guidelines for patients with unknown TSS recommend the use of MRI to determine eligibility for thrombolysis, but radiology assessments have high inter-reader variability. In this work, we present deep learning models that leverage MRI diffusion series to classify TSS based on clinically validated thresholds. We propose an intra-domain task-adaptive transfer learning method, which involves training a model on an easier clinical task (stroke detection) and then refining the model with different binary thresholds of TSS. We apply this approach to both 2D and 3D CNN architectures with our top model achieving an ROC-AUC value of 0.74, with a sensitivity of 0.70 and a specificity of 0.81 for classifying TSS < 4.5 h. Our pretrained models achieve better classification metrics than the models trained from scratch, and these metrics exceed those of previously published models applied to our dataset. Furthermore, our pipeline accommodates a more inclusive patient cohort than previous work, as we did not exclude imaging studies based on clinical, demographic, or image processing criteria. When applied to this broad spectrum of patients, our deep learning model achieves an overall accuracy of 75.78% when classifying TSS < 4.5 h, carrying potential therapeutic implications for patients with unknown TSS.

Indexed as

Brain IschemiaDeep LearningIschemic StrokeStrokeHumansMagnetic Resonance ImagingAcute ischemic strokeDeep learningStructural MRI

Identifiers

PMID33934065
PMCPMC13529409

What OpenQuestion holds

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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.