“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.
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.
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.
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.