Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study

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...

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Published in:Scientific reports 2023-05, Vol.13 (1), p.8834-8834, Article 8834
Main Authors: Bagcilar, Omer, Alis, Deniz, Alis, Ceren, Seker, Mustafa Ege, Yergin, Mert, Ustundag, Ahmet, Hikmet, Emil, Tezcan, Alperen, Polat, Gokhan, Akkus, Ahmet Tugrul, Alper, Fatih, Velioglu, Murat, Yildiz, Omer, Selcuk, Hakan Hatem, Oksuz, Ilkay, Kizilkilic, Osman, Karaarslan, Ercan
Format: Article
Language:English
Subjects:
639/705/794
692/699/375/1370
Angiography
Automation
Brain Ischemia
Cerebral Angiography - methods
Computed tomography
Computed Tomography Angiography - methods
Deep learning
Hospitals
Humanities and Social Sciences
Humans
Medical imaging
Middle Cerebral Artery
multidisciplinary
Patients
Retrospective Studies
Science
Science (multidisciplinary)
Stroke
Stroke - diagnostic imaging
Tomography
Tomography, X-Ray Computed
Veins & arteries
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-33723-w