ArticleAJNR. American journal of neuroradiology2018
Hybrid 3D/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT.
Article in AJNR. American journal of neuroradiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 4 of them syntheses that pooled it.
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Who cites it
98 citing papers in PubMed, 4 syntheses or guidelines pooled it, 274 citations in OpenAlex.
- Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Systematic Review of Artificial Intelligence for Abnormality Detection in High-volume Neuroimaging and Subgroup Meta-analysis for Intracranial Hemorrhage Detection.Clinical neuroradiology · 2023Pooled it
- Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?Medicine · 2022Pooled it
- Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis.La Radiologia medica · 2022Pooled it
- [Hematoma Segmentation of Spontaneous Intracerebral Hemorrhage Based on Watershed and Region-Growing Algorithm].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2022Trial
- 3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials.Neuroinformatics · 2021Trial
- Trial
- Deep Learning-Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model.JAMA network open · 2019Trial
- StrokeCT-2C5K: A Two-Center Cranial CT Dataset for Four-Class Stroke Classification Using SE-Attention-Enhanced Deep Learning Models.Biomimetics (Basel, Switzerland) · 2026Article
- Deep learning-assisted prediction of hydrocephalus in preoperative-subarachnoid hemorrhage: a multi-center study.Quantitative imaging in medicine and surgery · 2026Article
- MTW-ICHNet: Multi-task Weakly Supervised Learning with Enhanced Feature Descriptor Learning for Intracranial Hemorrhage Diagnosis.Journal of imaging informatics in medicine · 2026Article
- Advances in biomarkers for predicting outcomes in pediatric neurosurgical hemorrhagic conditions: a narrative review to enhance quality of care.Annals of medicine and surgery (2012) · 2026Review
- Article
- Performance of an artificial intelligence tool for multi-step acute stroke imaging: A multicenter diagnostic study.European journal of radiology open · 2025Article
- Intracranial hemorrhage segmentation and classification framework in computer tomography images using deep learning techniques.Scientific reports · 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
- Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time.AJNR. American journal of neuroradiology · 2025Article
- Adapting segment anything model for hematoma segmentation in traumatic brain injury.Discover imaging · 2025Article
- Diagnostic performance and clinical applications of artificial intelligence for intracranial bleeding detection: A meta-analysis.Brain & spine · 2025Article
- InceptionV4 and SEResNet101: precise predictors of intracranial hemorrhage and collateral circulation post-ischemic stroke intervention.Frontiers in neurology · 2025Article
38 more citing papers are in PubMed but not listed here.
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Authors and funding
15 authors at 8 institutions in 1 country.
Funding
Abstract
background and purposeConvolutional neural networks are a powerful technology for image recognition. This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT. MATERIALS AND
methodsThis study was performed in 2 phases. First, a training cohort of all NCCTs acquired at a single institution between January 1, 2017, and July 31, 2017, was used to develop and cross-validate a custom hybrid 3D/2D mask ROI-based convolutional neural network architecture for hemorrhage evaluation. Second, the trained network was applied prospectively to all NCCTs ordered from the emergency department between February 1, 2018, and February 28, 2018, in an automated inference pipeline. Hemorrhage-detection accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value were assessed for full and balanced datasets and were further stratified by hemorrhage type and size. Quantification was assessed by the Dice score coefficient and the Pearson correlation.
resultsA 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages) were used in this study. Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative-predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set. Dice scores for intraparenchymal hemorrhage, epidural/subdural hemorrhage, and SAH were 0.931, 0.863, and 0.772, respectively.
conclusionsA customized deep learning tool is accurate in the detection and quantification of hemorrhage on NCCT. Demonstrated high performance on prospective NCCTs ordered from the emergency department suggests the clinical viability of the proposed deep learning tool.
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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.