2023
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
KVAK, Daniel, Anna CHROMCOVÁ, Robert HRUBÝ, Eva JANŮ, Marek BIROŠ et. al.Základní údaje
Originální název
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
Název anglicky
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
Autoři
KVAK, Daniel, Anna CHROMCOVÁ, Robert HRUBÝ, Eva JANŮ, Marek BIROŠ, Marija PAJDAKOVIĆ, Karolína KVAKOVÁ, Mugahed AL-ANTARI, Pavlína POLÁŠKOVÁ a Sergei STRUKOV
Vydání
Diagnostics, Basel, Switzerland, MDPI, 2023, 2075-4418
Další údaje
Typ výsledku
Článek v odborném periodiku
Utajení
není předmětem státního či obchodního tajemství
Impakt faktor
Impact factor: 3.600 v roce 2022
Klíčová slova anglicky
convolutional neural network; computer-aided diagnosis; deep learning; object detection;lung cancer; pulmonary lesion; YOLO
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 20. 3. 2023 21:11, Mgr. Daniel Kvak
Anotace
V originále
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.