UNLABELLED: A fully-automated deep learning algorithm matched performance of radiologists in assessment of knee osteoarthritis severity in radiographs using the Kellgren-Lawrence grading system.
OBJECTIVES: Radiologists' perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond.
Journal of magnetic resonance imaging : JMRI
Mar 12, 2021
BACKGROUND: Several deep learning-based techniques have been developed for prostate cancer (PCa) detection using multiparametric magnetic resonance imaging (mpMRI), but few of them have been rigorously evaluated relative to radiologists' performance ...
Artificial intelligence (AI) systems play an increasingly important role in all parts of the imaging chain, from image creation to image interpretation to report generation. In order to responsibly manage radiology AI systems and make informed purcha...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 5, 2021
PURPOSE: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and large population studies have indicated that low-dose computed tomography (CT) screenin...
INTRODUCTION: We designed 5 convolutional neural network (CNN) models and ensemble models to differentiate malignant and benign thyroid nodules on CT, and compared the diagnostic performance of CNN models with that of radiologists.
Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radi...
OBJECTIVES: The aim is to offer an overview of the existing training programs and critically examine them and suggest avenues for further development of AI training programs for radiologists.
OBJECTIVE: To develop and evaluate a two-stage deep convolutional neural network system that mimics a radiologist's search pattern for detecting two small fractures: triquetral avulsion fractures and Segond fractures.
The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We u...