Evidence map›Paper›PMID 33330848›Full record

ArticleRadiology. Artificial intelligence2020

A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation.

David Dreizin, Yuyin Zhou, Shuhao Fu, Yan Wang, Guang Li, Kathryn Champ, Eliot Siegel, Ze Wang, Tina Chen, Alan L Yuille

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PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
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  5. The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset.Radiology. Artificial intelligence · 2024
    Article
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  18. Review
  19. Artificial Intelligence in Critical Care Medicine.Critical care (London, England) · 2022
    Review
  20. 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

10 authors.

David DreizinSection of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.).ORCID 0000-0002-0176-0912
Yuyin ZhouSection of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.).ORCID 0000-0003-2232-9563
Shuhao FuSection of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.).ORCID 0000-0002-0995-4922
Yan WangSection of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.).ORCID 0000-0002-1592-9627
Guang LiSection of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.).ORCID 0000-0002-9097-572X
Kathryn ChampSection of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.).ORCID 0000-0001-9948-3679
Eliot SiegelSection of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.).ORCID 0000-0002-7458-6281
Ze WangSection of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.).
Tina ChenSection of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.).
Alan L YuilleSection of Trauma and Emergency Radiology, R. Adams Cowley Shock Trauma Center (D.D.) and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, University of Maryland, 22 S Greene St, Baltimore, MD 21201 (G.L., K.C., E.S., Z.W., T.C.); and Department of Computer Science, Computational Cognition Vision and Learning, Johns Hopkins University, Baltimore, Md (Y.Z., S.F., Y.W., A.L.Y.).

Funding

Machine learning-based segmentation and risk modeling for real-time prediction of major arterial bleeding after pelvic fracturesK08EB027141 · NIBIB · UNIVERSITY OF MARYLAND BALTIMORE · PI DREIZIN, DAVID · 2019 to 2022
$745k
NIBIB NIH HHS K08 EB027141
6 · The paper itself

Abstract

purposeTo evaluate the feasibility of a multiscale deep learning algorithm for quantitative visualization and measurement of traumatic hemoperitoneum and to compare diagnostic performance for relevant outcomes with categorical estimation. MATERIALS AND

methodsThis retrospective, single-institution study included 130 patients (mean age, 38 years; interquartile range, 25-50 years; 79 men) with traumatic hemoperitoneum who underwent CT of the abdomen and pelvis at trauma admission between January 2016 and April 2019. Labeled cases were separated into five combinations of training (80%) and test (20%) sets, and fivefold cross-validation was performed. Dice similarity coefficients (DSCs) were compared with those from a three-dimensional (3D) U-Net and a coarse-to-fine deep learning method. Areas under the receiver operating characteristic curve (AUCs) for a composite outcome, including hemostatic intervention, transfusion, and in-hospital mortality, were compared with consensus categorical assessment by two radiologists. An optimal cutoff was derived by using a radial basis function-based support vector machine.

resultsMean DSC for the multiscale algorithm was 0.61 ± 0.15 (standard deviation) compared with 0.32 ± 0.16 for the 3D U-Net method and 0.52 ± 0.17 for the coarse-to-fine method (

conclusionA multiscale deep learning method for traumatic hemoperitoneum quantitative visualization had improved diagnostic performance for predicting hemorrhage-control interventions and mortality compared with subjective volume estimation.

Identifiers

PMID33330848
PMCPMC7706875

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