1 Ocak 2023 Dergi makalesi Açık Erişim

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

Dublin Core

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Bagcilar, Omer</dc:creator>
  <dc:creator>Alis, Deniz</dc:creator>
  <dc:creator>Alis, Ceren</dc:creator>
  <dc:creator>Seker, Mustafa Ege</dc:creator>
  <dc:creator>Yergin, Mert</dc:creator>
  <dc:creator>Ustundag, Ahmet</dc:creator>
  <dc:creator>Hikmet, Emil</dc:creator>
  <dc:creator>Tezcan, Alperen</dc:creator>
  <dc:creator>Polat, Gokhan</dc:creator>
  <dc:creator>Akkus, Ahmet Tugrul</dc:creator>
  <dc:creator>Alper, Fatih</dc:creator>
  <dc:creator>Velioglu, Murat</dc:creator>
  <dc:creator>Yildiz, Omer</dc:creator>
  <dc:creator>Selcuk, Hakan Hatem</dc:creator>
  <dc:creator>Oksuz, Ilkay</dc:creator>
  <dc:creator>Kizilkilic, Osman</dc:creator>
  <dc:creator>Karaarslan, Ercan</dc:creator>
  <dc:date>2023-01-01</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/266320</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:266320</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>SCIENTIFIC REPORTS 13(1) 9</dc:source>
  <dc:title>Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>