ArticleFrontiers in neurology2023
Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05378490 (The Akershus Study of Ischemic Stroke and Thrombolysis -1), which is not on this map. Cited by 8 papers.
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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.
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.
The Akershus Study of Ischemic Stroke and Thrombolysis -1 (ASIST-1) Epidemiology, Clinical and Radiological Presentation and Treatment of Cerebrovascular Disease and Stroke Mimics in a Norwegian Population
Who cites it
8 citing papers in PubMed.
- Clinical, Laboratory, and CT Morphological Factors Associated with 3-Month Functional Outcomes in Spontaneous Intracerebral Hemorrhage: A Retrospective Cohort Study.Brain sciences · 2026Article
- A multi-task deep learning pipeline for classification, detection, and weakly supervised 3D segmentation of intraparenchymal hematoma on brain CT.Scientific reports · 2026Article
- Automated volume estimate of the brain's lateral ventricles reveals a persistent association with cognitive performance among stroke survivors.Cerebral circulation - cognition and behavior · 2026Article
- Volume and Attenuation Characteristics of Chronic Subdural Hematoma: An Annotated Patient Cohort of 257 Patients with Interrater Reliability Assessments.Tomography (Ann Arbor, Mich.) · 2025Article
- Unsupervised semi-automated MRI segmentation detects cortical lesion expansion in chronic traumatic brain injury.Frontiers in neurology · 2025Article
- Near Infrared Spectroscopy as a Diagnostic Tool for Screening of Intracranial Hematomas; A Systematic Review and Meta-Analysis.Archives of academic emergency medicine · 2025Review
- Advantages of disposable portable endoscope in removing thalamic hematoma via the superior parietal lobule.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Article
- Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy.Brain sciences · 2024Review
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Radiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study. Methods: Non-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases. Results: The hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45-0.74; each Discussion: An open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.
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