Artificial Intelligence

“In a scenario where double reading at screening mammography is not available…we believe that the use of this model as a second reader could be beneficial,” wrote researchers in a new study published by Radiology.

You can find just about anything in an app store. Soon, that may include artificial intelligence applications for radiologists, as a recent Harvard Business Review article suggested.

Artificial neural networks (ANNs) can help radiologists classify pure ground glass opacities (GGOs), according to a new study published in Clinical Imaging. But they shouldn't rely solely on AI-produced findings.

Researchers created and validated a machine learning model using features taken from baseline, laboratory, electrocardiography (ECG), echocardiography and cardiovascular resonance (CMR) imaging data.

“Search for an aneurysm is one of the most labor-intensive and critical tasks radiologists undertake,” said co-senior author Kristen Yeom, MD, associate professor of radiology at Stanford University.

A new deep learning approach lowered radiation exposure from CT imaging while producing higher quality scans compared to traditional iterative reconstruction techniques, according to research published in Nature Machine Intelligence.

Deep convolutional neural networks (DCNNs) can better classify chest x-rays when trained on augmented datasets, according to a new study published in Clinical Radiology.

The Machine Learning Challenge on Pneumothorax Detection and Localization will kick-off at the SIIM 2019 Annual Meeting starting June 26 in Aurora, Colorado.

Machine learning can reduce a radiologists workload by lowering the number of screening mammograms they’re required to read while preserving accuracy, according to results of a feasibility study published in the Journal of the American College of Radiology.

Deep learning designed to read single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) can improve the diagnosis of coronary artery disease—a killer of more than 370,000 people in the U.S. annually.

The report, put out by the Journal of the American College of Radiology, is a companion roadmap to part one which was published April 16 in Radiology.

An AI-optimized American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS) improved risk stratification of thyroid nodules and may be easier for readers to use, according to a new study published in Radiology.