ArticleDiagnostics (Basel, Switzerland)2022
Merging Multiphase CTA Images and Training Them Simultaneously with a Deep Learning Algorithm Could Improve the Efficacy of AI Models for Lateral Circulation Assessment in Ischemic Stroke.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Artificial Intelligence-based diagnosis for facial fractures, advantages compared with conventional imaging diagnosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- Integrating hemodynamic analysis with traditional imaging in intracranial atherosclerotic stenosis: current status and future perspectives.Frontiers in neurology · 2025Review
- Deep learning-assisted diagnosis of large vessel occlusion in acute ischemic stroke based on four-dimensional computed tomography angiography.Frontiers in neuroscience · 2024Article
- Contemporary Methods for Detection and Intervention of Distal Medium and Small Vessel Occlusions.Journal of clinical medicine · 2023Review
- Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study.Scientific reports · 2023Article
- Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke.Tomography (Ann Arbor, Mich.) · 2023Article
- An Improved Detection Algorithm for Ischemic Stroke NCCT Based on YOLOv5.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
9 authors.
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Abstract
We aimed to build a deep learning-based, objective, fast, and accurate collateral circulation assessment model. We included 92 patients who had suffered acute ischemic stroke (AIS) with large vessel occlusion in the anterior circulation in this study, following their admission to our hospital from June 2020 to August 2021. We analyzed their baseline whole-brain four-dimensional computed tomography angiography (4D-CTA)/CT perfusion. The images of the arterial, arteriovenous, venous, and late venous phases were extracted from 4D-CTA according to the perfusion time-density curve. The subtraction images of each phase were created by subtracting the non-contrast CT. Each patient was marked as having good or poor collateral circulation. Based on the ResNet34 classification network, we developed a single-image input and a multi-image input network for binary classification of collateral circulation. The training and test sets included 65 and 27 patients, respectively, and Monte Carlo cross-validation was employed for five iterations. The network performance was evaluated based on its precision, accuracy, recall,
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