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.

Body mass index, systolic blood pressure and smoking behaviors mediate the effects of education on cardiovascular health, according to a recent study in The BMJ.

A team of researchers used MR spectroscopy to analyze the impact of traffic-related air pollution on childhood neurological development. Their results were published in Environmental Research.

Researchers at Wake Forest School of Medicine in Winston-Salem, North Carolina, believe they have taken an important first step in developing a brain-based test to diagnose autism, according to the results of an fMRI-based study published in Biological Psychology.

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.

The deep learning platform, tested on more than 6,000 cases from the National Lung Cancer Screening Trial and Northwestern University, performed similarly to six radiologists.