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

JSON-LD (schema.org)

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  "@context": "https://schema.org/", 
  "@id": 266320, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "Sisli Hamidiye Etfal Res & Training Hosp, Radiol Dept, Istanbul, Turkiye", 
      "name": "Bagcilar, Omer"
    }, 
    {
      "@type": "Person", 
      "name": "Alis, Deniz"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Istanbul Istinye State Hosp, Neurol Dept, Istanbul, Turkiye", 
      "name": "Alis, Ceren"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Acibadem Mehmet Ali Aydinlar Univ, Sch Med, Istanbul, Turkiye", 
      "name": "Seker, Mustafa Ege"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Hevi Hlth, Artificial Intelligence & Informat Technol, Istanbul, Turkiye", 
      "name": "Yergin, Mert"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkiye", 
      "name": "Ustundag, Ahmet"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkiye", 
      "name": "Hikmet, Emil"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Erzurum Ataturk Univ, Sch Med, Radiol Dept, Istanbul, Turkiye", 
      "name": "Tezcan, Alperen"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Erzurum Ataturk Univ, Sch Med, Radiol Dept, Istanbul, Turkiye", 
      "name": "Polat, Gokhan"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Erzurum Ataturk Univ, Sch Med, Radiol Dept, Istanbul, Turkiye", 
      "name": "Akkus, Ahmet Tugrul"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Erzurum Ataturk Univ, Sch Med, Radiol Dept, Istanbul, Turkiye", 
      "name": "Alper, Fatih"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Istanbul Fatih Sultan Mehmet Training & Res Hosp, Radiol Dept, Istanbul, Turkiye", 
      "name": "Velioglu, Murat"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Istanbul Fatih Sultan Mehmet Training & Res Hosp, Radiol Dept, Istanbul, Turkiye", 
      "name": "Yildiz, Omer"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Istanbul Bakirkoy Sadi Konuk Training & Res Hosp, Radiol Dept, Istanbul, Turkiye", 
      "name": "Selcuk, Hakan Hatem"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Istanbul Tech Univ, Comp Engn Dept, Istanbul, Turkiye", 
      "name": "Oksuz, Ilkay"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkiye", 
      "name": "Kizilkilic, Osman"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Acibadem Mehmet Ali Aydinlar Univ, Sch Med, Radiol Dept, Istanbul, Turkiye", 
      "name": "Karaarslan, Ercan"
    }
  ], 
  "datePublished": "2023-01-01", 
  "description": "<p>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.</p>", 
  "headline": "Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study", 
  "identifier": 266320, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "license": "http://www.opendefinition.org/licenses/cc-by", 
  "name": "Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study", 
  "url": "https://aperta.ulakbim.gov.tr/record/266320"
}