Evidence map›Paper›PMID 40414341›Full record

ArticleResuscitation2025

Automated detection of severe cerebral edema using explainable deep transfer learning after hypoxic ischemic brain injury.

Zihao Wang, Annelise M Kulpanowski, William A Copen, Eric S Rosenthal, Jacob A Dodelson, David E McCrory, Brian L Edlow, W Taylor Kimberly, Edilberto Amorim, M Brandon Westover and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Resuscitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Zihao WangAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
Annelise M KulpanowskiAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
William A CopenDepartment of Radiology, Neuroradiology Division, Massachusetts General Hospital, Boston, MA 02114, USA.
Eric S RosenthalDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
Jacob A DodelsonAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
David E McCroryAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
Brian L EdlowDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
W Taylor KimberlyDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
Edilberto AmorimDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA.
M Brandon WestoverDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA; Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
MingMing NingDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
Morteza ZabihiDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
Pamela W SchaeferDepartment of Radiology, Neuroradiology Division, Massachusetts General Hospital, Boston, MA 02114, USA.
Rajeev MalhotraDepartment of Medicine, Cardiology Division, Massachusetts General Hospital, Boston, MA 02114, USA.
Joseph T GiacinoDepartment of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Charlestown, MA 02129, USA.
David M GreerDepartment of Neurology, Boston University Chobanian and Avedisian School of Medicine, Boston Medical Center, Boston, MA 02118, USA.
Ona WuAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA. Electronic address: ona.wu@mgh.harvard.edu.

Funding

Late-onset Unexplained Epilepsy as a Risk Factor for DementiaR01NS130119 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Alice D Lam · 2023 to 2026
$8.7M
Data-Driven Sleep Biomarkers of Brain Health, Heart Health, and MortalityR01HL161253 · NHLBI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI CLIFFORD, GARI DAVID, MIGNOT, EMMANUEL J · 2022 to 2025
$8.3M
Multimodal Network Connectivity Architecture (MOCA) of the Brain and its Role in the Recovery of Consciousness in Comatose Cardiac Arrest PatientsR01NS102574 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI GREER, DAVID MATTHEW, WU, ONA · 2018 to 2022
$3.4M
Investigation of Sleep in the Intensive Care Unit (ICU-SLEEP)R01NS102190 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2018 to 2022
$3.2M
Prospective Validation of Neurophysiologic Outcome Prediction in Acute Brain InjuryR01NS126282 · NINDS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI Aaron F Struck, Michael Brandon Westover · 2023 to 2026
$2.9M
Big Data and Deep Learning for the Interictal-Ictal-Injury ContiuumR01NS107291 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2018 to 2022
$2.8M
Establishing a Brain Health Index from the Sleep ElectroencephalogramRF1NS120947 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI CASH, SYDNEY S, THOMAS, ROBERT JOSEPH · 2021 to 2021
$2.4M
Establishing a Brain Health IndexR01NS120947 · NINDS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI CASH, SYDNEY S, THOMAS, ROBERT JOSEPH · 2024 to 2025
$1.6M
NHLBI NIH HHS R01 HL161253NINDS NIH HHS R01 NS102190NINDS NIH HHS R01 NS102574NINDS NIH HHS R01 NS107291NINDS NIH HHS R01 NS120947NINDS NIH HHS R01 NS126282NINDS NIH HHS R01 NS130119NINDS NIH HHS RF1 NS120947
6 · The paper itself

Abstract

backgroundSubstantial gaps exist in the neuroprognostication of cardiac arrest patients who remain comatose after the restoration of spontaneous circulation. Most studies focus on predicting survival, a measure confounded by the withdrawal of life-sustaining treatment decisions. Severe cerebral edema (SCE) may serve as an objective proximal imaging-based surrogate of neurologic injury.

methodsWe retrospectively analyzed data from 288 patients to automate SCE detection with machine learning (ML) and to test the hypothesis that the quantitative values produced by these algorithms (ML_SCE) can improve predictions of neurologic outcomes. Ground-truth SCE (GT_SCE) classification was based on radiology reports.

resultsThe model attained a cross-validated testing accuracy of 87% [95% CI: 84%, 89%] for detecting SCE. Attention maps explaining SCE classification focused on cisternal regions (p < 0.05). Multivariable analyses showed that older age (p < 0.001), non-shockable initial cardiac rhythm (p = 0.004), and greater ML_SCE values (p < 0.001) were significant predictors of poor neurologic outcomes, with GT_SCE (p = 0.064) as a non-significant covariate.

conclusionOur results support the feasibility of automated SCE detection. Future prospective studies with standardized neurologic assessments are needed to substantiate the utility of quantitative ML_SCE values to improve neuroprognostication.

Indexed as

Brain EdemaDeep LearningHeart ArrestHypoxia-Ischemia, BrainAgedAlgorithmsFemaleHumansMaleMiddle AgedRetrospective StudiesCardiac arrestCerebral edemaComaDeep transfer machine learningExplainable artificial intelligenceQuantitative imaging biomarker

Identifiers

PMID40414341
PMCPMC12715856

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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.