Automated LVO Detection and Collateral Scoring on CTA using a 3D Self- Configuring Object Detection Network: A Multi-Center Study

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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“…Recent research by Omer Bagcilar employed a multicenter approach to validate a technology based on DL. [50] This technology utilized nnDetection, an advanced self-configuring 3D object detection network, which demonstrated over 98% accuracy in identifying LVO in an independent external dataset. In contrast to traditional image processing software (iSchemaView, RAPID CTA) that reported numerous false positives due to vascular asymmetry, commercially available DL software (such as Avicenna.ai's and e-Stroke) along with nnDetection showed higher diagnostic performance, exhibiting relatively high consistency with experienced radiologists.…”