ArticleJournal of digital imaging2021
Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep-learning-based non-contrast CT for detecting acute ischemic stroke: a systematic review and HSROC meta-analysis of patient-level diagnostic accuracy.BMC neurology · 2025Pooled it
- Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2024Pooled it
- StrokeCT-2C5K: A Two-Center Cranial CT Dataset for Four-Class Stroke Classification Using SE-Attention-Enhanced Deep Learning Models.Biomimetics (Basel, Switzerland) · 2026Article
- Integrating convolutional and transformer networks for precise diagnosis of watershed and hemorrhagic stroke.Scientific reports · 2025Article
- An effective brain stroke diagnosis strategy based on feature extraction and hybrid classifier.Scientific reports · 2025Article
- Sleep Disorders and Stroke: Pathophysiological Links, Clinical Implications, and Management Strategies.Medical sciences (Basel, Switzerland) · 2025Review
- Fusion Learning from Non-contrast CT Scans for the Detection of Hemorrhagic Transformation in Stroke Patients.Journal of imaging informatics in medicine · 2025Article
- Machine learning to predict penumbra core mismatch in acute ischemic stroke using clinical note data.NPJ digital medicine · 2025Article
- Preliminary study on AI-assisted diagnosis of bone remodeling in chronic maxillary sinusitis.BMC medical imaging · 2024Article
- Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions.Frontiers in robotics and AI · 2024Review
- Quantitative Measurement of Spinal Cerebrospinal Fluid by Cascade Artificial Intelligence Models in Patients with Spontaneous Intracranial Hypotension.Biomedicines · 2022Article
- Stroke Classification in Brain CT Images Using Transfer Learning With Fastai.Innovations in clinical neuroscienceArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute stroke is one of the leading causes of disability and death worldwide. Regarding clinical diagnoses, a rapid and accurate procedure is necessary for patients suffering from acute stroke. This study proposes an automatic identification scheme for acute ischemic stroke using deep convolutional neural networks (DCNNs) based on non-contrast computed tomographic (NCCT) images. Our image database for the classification model was composed of 1254 grayscale NCCT images from 96 patients (573 images) with acute ischemic stroke and 121 normal controls (681 images). According to the consensus of critical stroke findings by two neuroradiologists, a gold standard was established and used to train the proposed DCNN using machine-generated image features. Including the earliest DCNN, AlexNet, the popular Inception-v3, and ResNet-101 were proposed. To train the limited data size, transfer learning with ImageNet parameters was also used. The established models were evaluated by tenfold cross-validation and tested on an independent dataset containing 50 patients with acute ischemic stroke (108 images) and 58 normal controls (117 images) from another institution. AlexNet without pretrained parameters achieved an accuracy of 97.12%, a sensitivity of 98.11%, a specificity of 96.08%, and an area under the receiver operating characteristic curve (AUC) of 0.9927. Using transfer learning, transferred AlexNet, transferred Inception-v3, and transferred ResNet-101 achieved accuracies between 90.49 and 95.49%. Tested with a dataset from another institution, AlexNet showed an accuracy of 60.89%, a sensitivity of 18.52%, and a specificity of 100%. Transferred AlexNet, Inception-v3, and ResNet-101 achieved accuracies of 81.77%, 85.78%, and 80.89%, respectively. The proposed DCNN architecture as a computer-aided diagnosis system showed that training from scratch can generate a customized model for a specific scanner, and transfer learning can generate a more generalized model to provide diagnostic suggestions of acute ischemic stroke to radiologists.
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