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@article{2265618,
author = {Kvak, Daniel and Chromcová, Anna and Hrubý, Robert and Janů, Eva and Biroš, Marek and Pajdaković, Marija and Kvaková, Karolína and Alandantari, Mugahed and Polášková, Pavlína and Strukov, Sergei},
article_location = {Basel, Switzerland},
article_number = {6},
doi = {http://dx.doi.org/10.3390/diagnostics13061043},
keywords = {convolutional neural network; computer-aided diagnosis; deep learning; object detection;lung cancer; pulmonary lesion; YOLO},
issn = {2075-4418},
journal = {Diagnostics},
title = {Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images},
volume = {13},
year = {2023}
}
TY - JOUR
ID - 2265618
AU - Kvak, Daniel - Chromcová, Anna - Hrubý, Robert - Janů, Eva - Biroš, Marek - Pajdaković, Marija - Kvaková, Karolína - Al-antari, Mugahed - Polášková, Pavlína - Strukov, Sergei
PY - 2023
TI - Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images
JF - Diagnostics
VL - 13
IS - 6
PB - MDPI
SN - 20754418
KW - convolutional neural network
KW - computer-aided diagnosis
KW - deep learning
KW - object detection;lung cancer
KW - pulmonary lesion
KW - YOLO
N2 - Chest X-ray (CXR) is considered to be the most widely used modality for detecting and monitoring various thoracic findings, including lung carcinoma and other pulmonary lesions. However, X-ray imaging shows particular limitations when detecting primary and secondary tumors and is prone to reading errors due to limited resolution and disagreement between radiologists. To address these issues, we developed a deep-learning-based automatic detection algorithm (DLAD) to automatically detect and localize suspicious lesions on CXRs. Five radiologists were invited to retrospectively evaluate 300 CXR images from a specialized oncology center, and the performance of individual radiologists was subsequently compared with that of DLAD. The proposed DLAD achieved significantly higher sensitivity (0.910 (0.854-0.966)) than that of all assessed radiologists (RAD 10.290 (0.201-0.379), p < 0.001, RAD 20.450 (0.352-0.548), p < 0.001, RAD 30.670 (0.578-0.762), p < 0.001, RAD 40.810 (0.733-0.887), p = 0.025, RAD 50.700 (0.610-0.790), p < 0.001). The DLAD specificity (0.775 (0.717-0.833)) was significantly lower than for all assessed radiologists (RAD 11.000 (0.984-1.000), p < 0.001, RAD 20.970 (0.946-1.000), p < 0.001, RAD 30.980 (0.961-1.000), p < 0.001, RAD 40.975 (0.953-0.997), p < 0.001, RAD 50.995 (0.985-1.000), p < 0.001). The study results demonstrate that the proposed DLAD could be utilized as a decision-support system to reduce radiologists' false negative rate.
ER -
KVAK, Daniel, Anna CHROMCOVÁ, Robert HRUBÝ, Eva JANŮ, Marek BIROŠ, Marija PAJDAKOVI$\backslash$'C, Karolína KVAKOVÁ, Mugahed AL-ANTARI, Pavlína POLÁŠKOVÁ and Sergei STRUKOV. Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images. \textit{Diagnostics}. Basel, Switzerland: MDPI, 2023, vol.~13, No~6, 16 pp. ISSN~2075-4418. Available from: https://dx.doi.org/10.3390/diagnostics13061043.
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