Evidence map›Paper›PMID 38331959›Full record

ArticleAJNR. American journal of neuroradiology2024

A Clinical and Imaging Fused Deep Learning Model Matches Expert Clinician Prediction of 90-Day Stroke Outcomes.

Yongkai Liu, Preya Shah, Yannan Yu, Jai Horsey, Jiahong Ouyang, Bin Jiang, Guang Yang, Jeremy J Heit, Margy E McCullough-Hicks, Stephen M Hugdal and 6 more

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

Article in AJNR. American journal of neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
4.6field-weighted citation impact, top 5% 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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. 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

16 authors at 8 institutions in 3 countries.

Yongkai LiuFrom the Department of Radiology (Y.L., P.S., Y.Y., J.O., B.J., J.J.H., S.M.H., G.Z.), Stanford University, Stanford, Calfornia yongkliu@stanford.edu.ORCID 0000-0003-4478-4006
Preya ShahFrom the Department of Radiology (Y.L., P.S., Y.Y., J.O., B.J., J.J.H., S.M.H., G.Z.), Stanford University, Stanford, Calfornia.
Yannan YuFrom the Department of Radiology (Y.L., P.S., Y.Y., J.O., B.J., J.J.H., S.M.H., G.Z.), Stanford University, Stanford, Calfornia.
Jai HorseyMeharry Medical College (J.H.), Nashville, Tennessee.
Jiahong OuyangFrom the Department of Radiology (Y.L., P.S., Y.Y., J.O., B.J., J.J.H., S.M.H., G.Z.), Stanford University, Stanford, Calfornia.ORCID 0000-0002-0434-5757
Bin JiangFrom the Department of Radiology (Y.L., P.S., Y.Y., J.O., B.J., J.J.H., S.M.H., G.Z.), Stanford University, Stanford, Calfornia.ORCID 0000-0002-8507-0459
Guang YangNational Heart and Lung Institute (G.Y.), Imperial College London, London, UK.ORCID 0000-0001-7344-7733
Jeremy J HeitFrom the Department of Radiology (Y.L., P.S., Y.Y., J.O., B.J., J.J.H., S.M.H., G.Z.), Stanford University, Stanford, Calfornia.ORCID 0000-0003-1055-8000
Margy E McCullough-HicksDepartment of Neurology (M.E.M.-H.), University of Minnesota Medical School, Minneapolis, Minnesota.
Stephen M HugdalFrom the Department of Radiology (Y.L., P.S., Y.Y., J.O., B.J., J.J.H., S.M.H., G.Z.), Stanford University, Stanford, Calfornia.
Max WintermarkDepartment of Neuroradiology (M.W.), University of Texas MD Anderson Center, Houston, Texas.ORCID 0000-0002-6726-3951
Patrik MichelNeurology Service (P.M), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Switzerland.ORCID 0000-0003-4954-7579
David S LiebeskindDepartment of Neurology (D.S.L.), University of California, Los Angeles, Los Angeles, Calfornia.ORCID 0000-0002-5109-8736
Maarten G LansbergDepartment of Neurology (M.G.L., G.W.A.), Stanford, Stanford, Calfornia.ORCID 0000-0002-3545-6927
Gregory W AlbersDepartment of Neurology (M.G.L., G.W.A.), Stanford, Stanford, Calfornia.
Greg ZaharchukFrom the Department of Radiology (Y.L., P.S., Y.Y., J.O., B.J., J.J.H., S.M.H., G.Z.), Stanford University, Stanford, Calfornia.ORCID 0000-0001-5781-8848
Stanford University · USStanford Health Care · USLung Institute · USMeharry Medical College · USThe University of Texas MD Anderson Cancer Center · USUniversity of California, Los Angeles · USUniversity of Lausanne · CHUniversity of Minnesota Medical Center · US

Funding

Quantifying Collateral Perfusion in Cerebrovascular DiseaseR01NS066506 · NINDS · STANFORD UNIVERSITY · PI ZAHARCHUK, GREGORY GEORGE · 2009 to 2017
$4.9M
Predicting Tissue and Functional Outcome in Acute StrokeR01NS130172 · NINDS · STANFORD UNIVERSITY · PI Gregory George Zaharchuk · 2023 to 2026
$2.4M
Medical Research Council MC_PC_21013Medical Research Council MR/V023799/1NINDS NIH HHS R01 NS066506NINDS NIH HHS R01 NS130172
6 · The paper itself

Abstract

background and purposePredicting long-term clinical outcome in acute ischemic stroke is beneficial for prognosis, clinical trial design, resource management, and patient expectations. This study used a deep learning-based predictive model (DLPD) to predict 90-day mRS outcomes and compared its predictions with those made by physicians. MATERIALS AND

methodsA previously developed DLPD that incorporated DWI and clinical data from the acute period was used to predict 90-day mRS outcomes in 80 consecutive patients with acute ischemic stroke from a single-center registry. We assessed the predictions of the model alongside those of 5 physicians (2 stroke neurologists and 3 neuroradiologists provided with the same imaging and clinical information). The primary analysis was the agreement between the ordinal mRS predictions of the model or physician and the ground truth using the Gwet Agreement Coefficient. We also evaluated the ability to identify unfavorable outcomes (mRS >2) using the area under the curve, sensitivity, and specificity. Noninferiority analyses were undertaken using limits of 0.1 for the Gwet Agreement Coefficient and 0.05 for the area under the curve analysis. The accuracy of prediction was also assessed using the mean absolute error for prediction, percentage of predictions ±1 categories away from the ground truth (±1 accuracy [ACC]), and percentage of exact predictions (ACC).

resultsTo predict the specific mRS score, the DLPD yielded a Gwet Agreement Coefficient score of 0.79 (95% CI, 0.71-0.86), surpassing the physicians' score of 0.76 (95% CI, 0.67-0.84), and was noninferior to the readers (

conclusionsA deep learning method using acute clinical and imaging data for long-term functional outcome prediction in patients with acute ischemic stroke, the DLPD, was noninferior to that of clinical readers.

Indexed as

Deep LearningIschemic StrokeStrokeHumansPredictive Value of TestsPrognosis

Identifiers

PMID38331959
PMCPMC11288558
OpenAlexW4391640327

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.