ArticleScientific reports2023
Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Circle of Willis-Guided Localization for Simultaneous Detection and Classification of Large Vessel Occlusions in Brain CTA.Neuroinformatics · 2026Article
- Review
- Cerebral Infarction: Epidemiology, Classification, Mechanisms, Diagnosis, and Management.MedComm · 2026Review
- Large language models and large concept models in radiology: Present challenges, future directions, and critical perspectives.World journal of radiology · 2025Review
- Deep Learning Based Detection of Large Vessel Occlusions in Acute Ischemic Stroke Using High-Resolution Photon Counting Computed Tomography and Conventional Multidetector Computed Tomography.Clinical neuroradiology · 2025Article
- How AI and Robotics Will Advance Interventional Radiology: Narrative Review and Future Perspectives.Diagnostics (Basel, Switzerland) · 2024Review
- Review
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17 authors.
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Abstract
The use of deep learning (DL) techniques for automated diagnosis of large vessel occlusion (LVO) and collateral scoring on computed tomography angiography (CTA) is gaining attention. In this study, a state-of-the-art self-configuring object detection network called nnDetection was used to detect LVO and assess collateralization on CTA scans using a multi-task 3D object detection approach. The model was trained on single-phase CTA scans of 2425 patients at five centers, and its performance was evaluated on an external test set of 345 patients from another center. Ground-truth labels for the presence of LVO and collateral scores were provided by three radiologists. The nnDetection model achieved a diagnostic accuracy of 98.26% (95% CI 96.25-99.36%) in identifying LVO, correctly classifying 339 out of 345 CTA scans in the external test set. The DL-based collateral scores had a kappa of 0.80, indicating good agreement with the consensus of the radiologists. These results demonstrate that the self-configuring 3D nnDetection model can accurately detect LVO on single-phase CTA scans and provide semi-quantitative collateral scores, offering a comprehensive approach for automated stroke diagnostics in patients with LVO.
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