Evidence map›Paper›PMID 40582483›Full record

ArticleResuscitation2025

Machine learning to identify hypoxic-ischemic brain injury on early head CT after pediatric cardiac arrest.

Matthew P Kirschen, Jiren Li, Jonathan Elmer, Amirreza Manteghinejad, Dooman Arefan, Kathryn Graham, Ryan W Morgan, Vinay Nadkarni, Ramon Diaz-Arrastia, Robert Berg and 3 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

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

13 authors.

Matthew P KirschenDepartment of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA. Electronic address: kirschenm@chop.edu.
Jiren LiDepartments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
Jonathan ElmerDepartments of Emergency Medicine, Critical Care Medicine, and Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
Amirreza ManteghinejadDepartment of Radiology, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Dooman ArefanDepartments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
Kathryn GrahamDepartment of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Ryan W MorganDepartment of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Vinay NadkarniDepartment of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Ramon Diaz-ArrastiaDepartment of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Robert BergDepartment of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Alexis TopjianDepartment of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Arastoo VossoughDepartment of Radiology, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Shandong WuDepartments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: wus3@upmc.edu.

Funding

SCH: Leverage clinical knowledge to augment deep learning analysis of breast imagesR01EB032896 · NIBIB · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI WU, SHANDONG · 2021 to 2024
$1.4M
Cerebral autoregulation and MRI measures of brain injury after pediatric-post cardiac arrestK23NS116120 · NINDS · CHILDREN'S HOSP OF PHILADELPHIA · PI KIRSCHEN, MATTHEW P · 2021 to 2025
$966k
NIBIB NIH HHS R01 EB032896NINDS NIH HHS K23 NS116120
6 · The paper itself

Abstract

aimsTo train deep learning models to detect hypoxic-ischemic brain injury (HIBI) on early CT scans after pediatric out-of-hospital cardiac arrest (OHCA) and determine if models could identify HIBI that was not visually appreciable to a radiologist.

methodsRetrospective study of children who had a CT scan within 24 h of OHCA compared to age-matched controls. We designed models to detect HIBI by discriminating CT images from OHCA cases and controls, and predict death and unfavorable outcome (PCPC 4-6 at hospital discharge) among cases. Model performance was measured by AUC. We trained a second model to distinguish OHCA cases with radiologist-identified HIBI from controls without OHCA and tested the model on OHCA cases without radiologist-identified HIBI. We compared outcomes between OHCA cases with and without model-categorized HIBI.

resultsWe analyzed 117 OHCA cases (age 3.1 [0.7-12.2] years); 43 % died and 58 % had unfavorable outcome. Median time from arrest to CT was 2.1 [1.0,7.2] hours. Deep learning models discriminated OHCA cases from controls with a mean AUC of 0.87 ± 0.05. Among OHCA cases, mean AUCs for predicting death and unfavorable outcome were 0.79 ± 0.06 and 0.69 ± 0.06, respectively. Mean AUC was 0.98 ± 0.01 for discriminating between 44 OHCA cases with radiologist-identified HIBI and controls. Among 73 OHCA cases without radiologist-identified HIBI, the model identified 36 % as having presumed HIBI; 31 % of whom died compared to 17 % of cases without HIBI identified radiologically and via the model (p = 0.174).

conclusionDeep learning models can identify HIBI on early CT images after pediatric OHCA and detect some presumed HIBI visually not identified by a radiologist.

Indexed as

Deep LearningHypoxia-Ischemia, BrainMachine LearningOut-of-Hospital Cardiac ArrestTomography, X-Ray ComputedCardiopulmonary ResuscitationChildChild, PreschoolFemaleHumansInfantMaleRetrospective StudiesCardiac arrestComputerized tomographyMachine learningNeuroimagingPediatrics

Identifiers

PMID40582483
PMCPMC12233190

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.