ArticleJournal of X-ray science and technology2023
Ischemic stroke subtyping method combining convolutional neural network and radiomics.
Article in Journal of X-ray science and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- AGTS-Net: Anatomically Guided Two-Stage Network for Actionable Stroke Classification in Complete NCCT Studies.Bioengineering (Basel, Switzerland) · 2026Article
- Correlation between myocardial radiomics features at the left atrium-left atrial appendage junction and stroke risk in atrial fibrillation patients: a retrospective case-control study.Frontiers in cardiovascular medicine · 2026Article
- Predicting Stroke Etiology with Radiomics: A Retrospective Study.Medical sciences (Basel, Switzerland) · 2025Article
- Automatic etiological classification of stroke thrombus digital photographs using a deep learning model.Frontiers in neurology · 2025Article
- Exploration of the shared gene signatures and molecular mechanisms between cardioembolic stroke and ischemic stroke.Frontiers in neurology · 2025Article
- The impracticality of radiomics research in acute ischemic stroke: from the perspective of primary healthcare institutions.Frontiers in neurology · 2025Review
- Deep learning biomarker of chronometric and biological ischemic stroke lesion age from unenhanced CT.NPJ digital medicine · 2024Article
- Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease.Frontiers in human neuroscience · 2023Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiogenic embolism (CE) and large-artery atherosclerosis embolism (LAA) are the two most common ischemic stroke (IS) subtypes.
objectiveIn order to assist doctors in the precise diagnosis and treatment of patients, this study proposed an IS subtyping method combining convolutional neural networks (CNN) and radiomics.
methodsFirstly, brain embolism regions were segmented from the computed tomography angiography (CTA) images, and radiomics features were extracted; Secondly, the extracted radiomics features were optimized with the L2 norm, and the feature selection was performed by combining random forest; then, the CNN Cap-UNet was built to extract the deep learning features of the last layer of the network; Finally, combining the selected radiomics features and deep learning features, 9 small-sample classifiers were trained respectively to build and select the optimal IS subtyping classification model.
resultsThe experimental data include CTA images of 82 IS patients diagnosed and treated in Shanghai Sixth People's Hospital. The AUC value and accuracy of the optimal subtyping model based on the Adaboost classifier are 0.9018 and 0.8929, respectively.
conclusionThe experimental results show that the proposed method can effectively predict the subtype of IS and has potential to assist doctors in making timely and accurate diagnoses of IS patients.
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