Evidence map›Paper›PMID 40046237›Full record

ArticleApplied sciences (Basel, Switzerland)2025

Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography.

Anh T Tran, Dmitriy Desser, Tal Zeevi, Gaby Abou Karam, Julia Zietz, Andrea Dell'Orco, Min-Chiun Chen, Ajay Malhotra, Adnan I Qureshi, Santosh B Murthy and 5 more

Abstract read
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In one paragraph

Article in Applied sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

15 authors.

Anh T TranDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519, USA.
Dmitriy DesserDepartment of Neuroradiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, 10117 Berlin, Germany.ORCID 0000-0002-4642-3063
Tal ZeeviDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519, USA.
Gaby Abou KaramDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519, USA.ORCID 0009-0003-3540-7185
Julia ZietzDepartment of Neuroradiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, 10117 Berlin, Germany.
Andrea Dell'OrcoDepartment of Neuroradiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, 10117 Berlin, Germany.ORCID 0000-0002-3964-8360
Min-Chiun ChenDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519, USA.
Ajay MalhotraDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519, USA.ORCID 0000-0001-9223-6640
Adnan I QureshiZeenat Qureshi Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO 65201, USA.
Santosh B MurthyDepartment of Neurology, Weill Cornell School of Medicine, New York, NY 10065, USA.ORCID 0000-0002-4950-0992
Shahram MajidiDepartment of Neurosurgery, Mount Sinai School of Medicine, New York, NY 10029, USA.ORCID 0000-0003-2971-6216
Guido J FalconeDepartment of Neurology, Yale School of Medicine, New Haven, CT 06510, USA.ORCID 0000-0002-6407-0302
Kevin N ShethDepartment of Neurology, Yale School of Medicine, New Haven, CT 06510, USA.
Jawed NawabiDepartment of Neuroradiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, 10117 Berlin, Germany.ORCID 0000-0002-1137-0643
Seyedmehdi PayabvashDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Radiomics Signatures and Patient Outcomes in Intracerebral HemorrhageK23NS118056 · NINDS · YALE UNIVERSITY · PI Seyedmehdi Payabvash · 2021 to 2026
$780k
Doris Duke Charitable Foundation 2020097NCATS NIH HHS UL1 TR001863NINDS NIH HHS K23 NS118056
6 · The paper itself

Abstract

Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to generate probability measures for false HE classifications. With this strategy, we can limit expert review of automated hematoma segmentations to a subset of the dataset, tailored to the research team's preferred sensitivity or specificity thresholds and their tolerance for false-positive versus false-negative results. We utilized three separate multicentric cohorts for cross-validation/training, internal testing, and external validation (

Indexed as

classificationconvolution neural networkground truth generationhematoma expansionhigh sensitivity modelintracerebral hemorrhagesegmentation

Identifiers

PMID40046237
PMCPMC11882137

What OpenQuestion holds

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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.