ArticleEuropean radiology2022
Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Systematic review of commercial artificial intelligence tools for the detection and volume quantification in intracerebral hemorrhage.European radiology · 2026Pooled it
- Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Diagnostic performance and confidence of an optimized deep-learning algorithm for the detection of intracranial hemorrhages.Insights into imaging · 2026Article
- AI-assisted hemorrhage detection following endovascular stroke treatment: a retrospective diagnostic accuracy study.Neurological research and practice · 2026Article
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- Radiomics-Based AI Model to Assist Clinicians in Intracranial Hemorrhage Diagnosis: External Validation Study.JMIR formative research · 2025Article
- Counting coins in the dark-Austrian, German, and Swiss medical students' perceptions of radiology.European radiology · 2025Article
- Prospective Evaluation of Artificial Intelligence Imaging Support Software for Acute Ischemic Stroke in the Mayo Clinic Telestroke Network.Mayo Clinic proceedings. Innovations, quality & outcomes · 2025Article
- Diagnostic Accuracy of Deep Learning for Intracranial Hemorrhage Detection in Non-Contrast Brain CT Scans: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Development and validation of intracranial hypertension prediction models based on radiomic features in patients with traumatic brain injury: an exploratory study based on CENTER-TBI data.Critical care (London, England) · 2025Article
- An Artificial Intelligence Algorithm Integrated into the Clinical Workflow Can Ensure High Quality Acute Intracranial Hemorrhage CT Diagnostic.Clinical neuroradiology · 2025Article
- Diagnostic performance and clinical applications of artificial intelligence for intracranial bleeding detection: A meta-analysis.Brain & spine · 2025Article
- Current Stroke Solutions Using Artificial Intelligence: A Review of the Literature.Brain sciences · 2024Review
- Deep learning-based defacing tool for CT angiography: CTA-DEFACE.European radiology experimental · 2024Article
- Do Deep Learning Algorithms Accurately Segment Intracerebral Hemorrhages on Noncontrast Computed Tomography? A Systematic Review and Meta-Analysis.Stroke (Hoboken, N.J.) · 2024Article
- Improved differentiation of cavernous malformation and acute intraparenchymal hemorrhage on CT using an AI algorithm.Scientific reports · 2024Article
- Automated volumetric evaluation of intracranial compartments and cerebrospinal fluid distribution on emergency trauma head CT scans to quantify mass effect.Frontiers in neuroscience · 2024Article
- Clinical Impact of an AI Decision Support System for Detection of Intracranial Hemorrhage in CT Scans.Neurotrauma reports · 2024Article
- Artificial Intelligence-based automated CT brain interpretation to accelerate treatment for acute stroke in rural India: An interrupted time series study.PLOS global public health · 2024Article
- Regarding "Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model".AJNR. American journal of neuroradiology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesArtif icial intelligence (AI)-based image analysis is increasingly applied in the acute stroke field. Its implementation for the detection and quantification of hemorrhage suspect hyperdensities in non-contrast-enhanced head CT (NCCT) scans may facilitate clinical decision-making and accelerate stroke management.
methodsNCCTs of 160 patients with suspected acute stroke were analyzed regarding the presence or absence of acute intracranial hemorrhages (ICH) using a novel AI-based algorithm. Read was performed by two blinded neuroradiology residents (R1 and R2). Ground truth was established by an expert neuroradiologist. Specificity, sensitivity, and area under the curve were calculated for ICH and intraparenchymal hemorrhage (IPH) detection. IPH-volumes were segmented and quantified automatically by the algorithm and semi-automatically. Intraclass correlation coefficient (ICC) and Dice coefficient (DC) were calculated.
resultsIn total, 79 of 160 patients showed acute ICH, while 47 had IPH. Sensitivity and specificity for ICH detection were 0.91 and 0.89 for the algorithm; 0.99 and 0.98 for R1; and 1.00 and 0.98 for R2. Sensitivity and specificity for IPH detection were 0.98 and 0.89 for the algorithm; 0.83 and 0.99 for R1; and 0.91 and 0.99 for R2. Interreader reliability for ICH and IPH detection showed strong agreements for the algorithm (0.80 and 0.84), R1 (0.96 and 0.84), and R2 (0.98 and 0.92), respectively. ICC indicated an excellent (0.98) agreement between the algorithm and the reference standard of the IPH-volumes. The mean DC was 0.82.
conclusionThe AI-based algorithm reliably assessed the presence or absence of acute ICHs in this dataset and quantified IPH volumes precisely. KEY POINTS: • Artificial intelligence (AI) is able to detect hyperdense volumes on brain CTs reliably. • Sensitivity and specificity are highest for the detection of intraparenchymal hemorrhages. • Interreader reliability for hemorrhage detection shows strong agreement for AI and human readers.
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