ArticleDiagnostics (Basel, Switzerland)2020
Automatic Assessment of ASPECTS Using Diffusion-Weighted Imaging in Acute Ischemic Stroke Using Recurrent Residual Convolutional Neural Network.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 39 citations in OpenAlex.
- Mixture-of-Skip-Connection Deep Learning Model to Classify Stroke Severity from Diffusion Weighted Imaging Based on NIHSS.Journal of imaging informatics in medicine · 2026Article
- Automated quantification of the Alberta Stroke Programme Early CT Score (ASPECTS) and infarct core on diffusion-weighted imaging in acute ischaemic stroke: multi-centre validation and severity stratification.Brain communications · 2026Article
- Integrating convolutional and transformer networks for precise diagnosis of watershed and hemorrhagic stroke.Scientific reports · 2025Article
- Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging.BioMedInformatics · 2025Article
- Diffusion-weighted imaging and the Alberta Stroke Program Early CT Score (DWI-ASPECTS)-guided intra-arterial thrombectomy beyond 6 hours: feasibility, substantial efficacy, and acceptable safety.American journal of translational research · 2025Article
- Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies.Japanese journal of radiology · 2024Article
- Artificial Intelligence in Stroke Imaging: A Comprehensive Review.The Eurasian journal of medicine · 2023Article
- Automatic Alberta Stroke Program Early Computed Tomographic Scoring in patients with acute ischemic stroke using diffusion-weighted imaging.Medical & biological engineering & computing · 2023Article
- Deep learning for collateral evaluation in ischemic stroke with imbalanced data.International journal of computer assisted radiology and surgery · 2023Article
- Deep convolutional neural networks for multiple histologic types of ovarian tumors classification in ultrasound images.Frontiers in oncology · 2023Article
- Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms.Tomography (Ann Arbor, Mich.) · 2022Article
- Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.BioMed research international · 2022Review
- Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.Acta neurochirurgica. Supplement · 2022Article
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Authors and funding
6 authors at 1 institution in 1 country.
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Abstract
The early detection and rapid quantification of acute ischemic lesions play pivotal roles in stroke management. We developed a deep learning algorithm for the automatic binary classification of the Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) using diffusion-weighted imaging (DWI) in acute stroke patients. Three hundred and ninety DWI datasets with acute anterior circulation stroke were included. A classifier algorithm utilizing a recurrent residual convolutional neural network (RRCNN) was developed for classification between low (1-6) and high (7-10) DWI-ASPECTS groups. The model performance was compared with a pre-trained VGG16, Inception V3, and a 3D convolutional neural network (3DCNN). The proposed RRCNN model demonstrated higher performance than the pre-trained models and 3DCNN with an accuracy of 87.3%, AUC of 0.941, and F1-score of 0.888 for classification between the low and high DWI-ASPECTS groups. These results suggest that the deep learning algorithm developed in this study can provide a rapid assessment of DWI-ASPECTS and may serve as an ancillary tool that can assist physicians in making urgent clinical decisions.
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