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@proceedings{2265617,
author = {Kvak, Daniel and Chromcová, Anna and Hrubý, Robert and Janů, Eva and Biroš, Marek and Pajdaković, Marija and Kvaková, Karolína and Polášková, Pavlína and Strukov, Sergei},
booktitle = {European Congress of Radiology 2023, Vienna},
language = {eng},
title = {Leveraging Deep Learning Decision-Support System in Specialized Oncology Center},
url = {https://epos.myesr.org/poster/esr/ecr2023/C-24305},
year = {2023}
}
TY - CONF
ID - 2265617
AU - Kvak, Daniel - Chromcová, Anna - Hrubý, Robert - Janů, Eva - Biroš, Marek - Pajdaković, Marija - Kvaková, Karolína - Polášková, Pavlína - Strukov, Sergei
PY - 2023
TI - Leveraging Deep Learning Decision-Support System in Specialized Oncology Center
UR - https://epos.myesr.org/poster/esr/ecr2023/C-24305
N2 - Chest X-ray (CXR) is one of the most common diagnostic imaging tests to identify and monitor various chest findings, including pulmonary lesions that may be indicative of, among other pathologies, lung cancer. However, the effectiveness of X-ray imaging in detecting both primary and secondary tumors is not always reliable. The aim of our study was to demonstrate the effectiveness of the proposed deep learning-based algorithm (DLAD) for detecting pulmonary lesions on CXR images and to compare its performance with that of radiologists with different levels of experience in a simulated clinical setting. The proposed DLAD demonstrated improved detection performance compared to existing conventional imaging-based diagnostics, as it showed a significantly lower false-negative rate while also providing relatively high specificity.
ER -
KVAK, Daniel, Anna CHROMCOVÁ, Robert HRUBÝ, Eva JANŮ, Marek BIROŠ, Marija PAJDAKOVI$\backslash$'C, Karolína KVAKOVÁ, Pavlína POLÁŠKOVÁ a Sergei STRUKOV. Leveraging Deep Learning Decision-Support System in Specialized Oncology Center. In \textit{European Congress of Radiology 2023, Vienna}. 2023.
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