Acute ischemic stroke (AIS) is a critical medical emergency that occurs when blood flow to a part of the brain is blocked, usually due to a clot. In the United States, AIS is ranked as the fifth leading cause of death, and a significant number of these cases are associated with large vessel occlusions (LVOs). LVOs are blockages in the brain’s major arteries and are particularly dangerous because they affect large regions of the brain. This results in a higher risk of long-term disability and mortality compared to other types of strokes. The timely and accurate detection of LVOs is vital to ensuring patients receive appropriate treatments, such as mechanical thrombectomy, within the narrow therapeutic window. Unfortunately, current clinical protocols depend heavily on the manual interpretation of computed tomography angiography (CTA) scans by expert radiologists. In many settings, especially in under-resourced hospitals or during off-hours, the availability of such specialists is limited, leading to potentially life-threatening delays in diagnosis and treatment.
To address this critical issue, our research proposes an automated approach to LVO detection using advanced deep learning techniques. We introduce the Deep Residual Dilated Convolutional Neural Network (DRDCNet-3D), a model designed specifically to analyze 3D CTA brain images for LVO identification. A key innovation of DRDCNet-3D lies in its use of dilated convolutions, which are a type of convolutional operation that expands the receptive field of the model without increasing the number of parameters or sacrificing resolution. This allows the network to effectively capture fine-grained vascular structures and spatial features critical for detecting occlusions. Furthermore, the use of residual connections in the network helps mitigate the vanishing gradient problem and promotes more effective training, especially in deeper architectures. By leveraging these architectural advancements, DRDCNet-3D is able to learn complex patterns from CTA scans, thus enabling it to outperform traditional 2D models or those with basic convolutional designs.
We validated our proposed method using the IACTA-EST dataset, a robust collection of CTA scans curated specifically for stroke research. The DRDCNet-3D achieved an AUC-ROC of 0.91 and an F1-score of 0.90, marking a significant improvement over existing models and manual diagnostic approaches. These results demonstrate the model's strong ability to differentiate between occluded and non-occluded vessels with high precision and recall. More importantly, this technology holds real-world clinical value: it can support physicians in early identification of LVOs, enabling quicker decision-making and initiation of treatment protocols such as thrombolysis or thrombectomy. By integrating such AI-driven tools into stroke management pipelines, healthcare systems can potentially reduce time-to-treatment, improve patient prognosis, and lessen the psychological and functional burdens that stroke survivors often face. Our findings highlight the transformative potential of AI in acute stroke care, especially in scenarios where rapid diagnosis is essential but expert resources are limited.
International Awards on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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