Purpose To evaluate the performance of a deep learning-based computer-aided detection
(CAD) software for detecting pulmonary nodules, masses, and consolidation on chest …

Purpose Computer-aided diagnosis (CAD), which assists in the interpretation of chest
radiographs, is becoming common. However, few studies have evaluated the benefits and …

Along with recent developments in deep learning techniques, computer-aided diagnosis
(CAD) has been growing rapidly in the medical imaging field. In this work, we evaluate the …

Purpose To evaluate the performance of a deep learning-based computer-aided diagnosis
(CAD) system at detecting pulmonary nodules on CT by comparing radiologists' readings …

Objectives To evaluate the clinical impact of a deep learning system (DLS) for automated
detection of pulmonary nodules on computed tomography (CT) images as a second reader …

Background Multicenter studies are required to validate the added benefit of using deep
convolutional neural network (DCNN) software for detecting malignant pulmonary nodules …

Background Previous studies assessing the effects of computer-aided detection on observer
performance in the reading of chest radiographs used a sequential reading design that may …

Objective To explore the value of a deep learning-based algorithm in detecting Lung CT
Screening Reporting and Data System category 4 nodules on chest radiographs from an …

Objective To assess a deep learning-based artificial intelligence model for the detection of
pulmonary nodules on chest radiographs and to compare its performance with board …

Objectives To compare the performance of radiologists in characterizing and diagnosing
pulmonary nodules/masses with and without deep learning (DL)–based computer-aided …