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
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Alis, Deniz</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Alis, Ceren</subfield> <subfield code="u">Istanbul Istinye State Hosp, Neurol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Seker, Mustafa Ege</subfield> <subfield code="u">Acibadem Mehmet Ali Aydinlar Univ, Sch Med, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Yergin, Mert</subfield> <subfield code="u">Hevi Hlth, Artificial Intelligence & Informat Technol, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Ustundag, Ahmet</subfield> <subfield code="u">Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Hikmet, Emil</subfield> <subfield code="u">Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Tezcan, Alperen</subfield> <subfield code="u">Erzurum Ataturk Univ, Sch Med, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Polat, Gokhan</subfield> <subfield code="u">Erzurum Ataturk Univ, Sch Med, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Akkus, Ahmet Tugrul</subfield> <subfield code="u">Erzurum Ataturk Univ, Sch Med, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Alper, Fatih</subfield> <subfield code="u">Erzurum Ataturk Univ, Sch Med, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Velioglu, Murat</subfield> <subfield code="u">Istanbul Fatih Sultan Mehmet Training & Res Hosp, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Yildiz, Omer</subfield> <subfield code="u">Istanbul Fatih Sultan Mehmet Training & Res Hosp, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Selcuk, Hakan Hatem</subfield> <subfield code="u">Istanbul Bakirkoy Sadi Konuk Training & Res Hosp, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Oksuz, Ilkay</subfield> <subfield code="u">Istanbul Tech Univ, Comp Engn Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Kizilkilic, Osman</subfield> <subfield code="u">Istanbul Univ Cerrahpasa, Cerrahpasa Med Fac, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Karaarslan, Ercan</subfield> <subfield code="u">Acibadem Mehmet Ali Aydinlar Univ, Sch Med, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="909" ind1="C" ind2="4"> <subfield code="p">SCIENTIFIC REPORTS</subfield> <subfield code="v">13</subfield> <subfield code="n">1</subfield> <subfield code="c">9</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-tubitak-destekli-proje-yayinlari</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="a">Creative Commons Attribution</subfield> <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.1038/s41598-023-33723-w</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="a">Bagcilar, Omer</subfield> <subfield code="u">Sisli Hamidiye Etfal Res & Training Hosp, Radiol Dept, Istanbul, Turkiye</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="o">oai:aperta.ulakbim.gov.tr:266320</subfield> <subfield code="p">user-tubitak-destekli-proje-yayinlari</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="2">opendefinition.org</subfield> <subfield code="a">cc-by</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2023-01-01</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="u">https://aperta.ulakbim.gov.trrecord/266320/files/bib-37afdc3a-785d-491d-9845-a1b186987361.txt</subfield> <subfield code="z">md5:ec9cb326051581ba6c3edb6551c47d9c</subfield> <subfield code="s">384</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <controlfield tag="005">20240607120447.0</controlfield> <controlfield tag="001">266320</controlfield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">article</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><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></subfield> </datafield> </record>
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