Importance An increasing number of machine learning (ML)–based clinical decision support
systems (CDSSs) are described in the medical literature, but this research focuses almost …

Artificial intelligence is a hot topic in medical imaging. The development of deep learning
methods and in particular the use of convolutional neural networks (CNNs), have led to …

Objectives CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19)
pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) …

To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists,
many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are …

AIM To compare the performance and reading time of different readers using automatic
artificial intelligence (AI)-powered computer-aided detection (CAD) to detect lung nodules in …

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 …

This work presents a novel method that uses a Vector of Pre-processing Filters combined
with simple relational and Boolean equations for pulmonary nodule detection. To isolate …

Background Artificial intelligence (AI) products have been widely used for the clinical
detection of primary lung tumors. However, their performance and accuracy in risk prediction …

In healthcare, integration of artificial intelligence (AI) holds strong promise for facilitating
clinicians' work, especially in clinical imaging. We aimed to assess the impact of AI …

Automated detection and segmentation is a prerequisite for the deployment of image‐based
secondary analyses, especially for lung tumors. However, currently only applications for …