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

Citation Style Language JSON

{
  "DOI": "10.1038/s41598-023-33723-w", 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Bagcilar", 
      "given": " Omer"
    }, 
    {
      "family": "Alis", 
      "given": " Deniz"
    }, 
    {
      "family": "Alis", 
      "given": " Ceren"
    }, 
    {
      "family": "Seker", 
      "given": " Mustafa Ege"
    }, 
    {
      "family": "Yergin", 
      "given": " Mert"
    }, 
    {
      "family": "Ustundag", 
      "given": " Ahmet"
    }, 
    {
      "family": "Hikmet", 
      "given": " Emil"
    }, 
    {
      "family": "Tezcan", 
      "given": " Alperen"
    }, 
    {
      "family": "Polat", 
      "given": " Gokhan"
    }, 
    {
      "family": "Akkus", 
      "given": " Ahmet Tugrul"
    }, 
    {
      "family": "Alper", 
      "given": " Fatih"
    }, 
    {
      "family": "Velioglu", 
      "given": " Murat"
    }, 
    {
      "family": "Yildiz", 
      "given": " Omer"
    }, 
    {
      "family": "Selcuk", 
      "given": " Hakan Hatem"
    }, 
    {
      "family": "Oksuz", 
      "given": " Ilkay"
    }, 
    {
      "family": "Kizilkilic", 
      "given": " Osman"
    }, 
    {
      "family": "Karaarslan", 
      "given": " Ercan"
    }
  ], 
  "container_title": "SCIENTIFIC REPORTS", 
  "id": "266320", 
  "issue": "1", 
  "issued": {
    "date-parts": [
      [
        2023, 
        1, 
        1
      ]
    ]
  }, 
  "page": "9", 
  "title": "Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study", 
  "type": "article-journal", 
  "volume": "13"
}