Artificial Intelligence

The American College of Radiology Data Science Institute (ACR DSI) announced Oct. 26 the release of a series of standardized artificial intelligence (AI) use cases to advance imaging in AI.

A team of Japanese researchers found a deep learning-based algorithm used to analyze time-of-flight (TOF) MR angiography images improved cerebral aneurysm detection with an average sensitivity of 92 percent compared to initial radiology reports, according to research published Oct. 23 in Radiology.

When imaging brain tumors such as gliomas, machine learning may advance the use of imaging and augment clinical care for patients, according to a review published Oct. 17 in the American Journal of Roentgenology—specifically in tumor segmentation and MRI radiomics.

“For those who are unfamiliar with the field of machine learning (ML), the emerging research can be daunting, with a wide variation in the terms used and the metrics presented,” wrote Guy S. Handelman, with Belfast City Hospital in Northern Ireland, U.K., in a recent AJR perspective.

A team of East Coast researchers found the effectiveness of artificial intelligence (AI)-based decision support (DS) systems depends on how they are presented in a radiologist’s clinical workflow.

Breast imagers and artificial intelligence (AI) experts have shown that a new AI algorithm measures breast density with accuracy comparable to an experienced breast imager, according to new research published online Oct. 16 in Radiology.

William Hsu, PhD, a biomedical informatician and associate professor of radiology at UCLA, has been named deputy editor for the Radiological Society of North America (RSNA)’s new journal, Radiology: Artificial Intelligence.

Artificial intelligence (AI) models utilizing radiologist-provided BI-RADS classification outperformed methods that did not use them, according to an Oct. 15 study in the Journal of the American College of Radiology.

A novel convolutional neural network (CNN) approach could be used to help radiologists improve their classification of osteoarthritis (OA) on knee radiographs, reported authors of a new study published in the Journal of Digital Imaging.

Deep learning may accurately identify false-positive mammograms and distinguish such from images identified as malignant or negative, according to new research published Oct. 11 in the journal Clinical Cancer Research.

A team of California researchers used a deep convolutional neural network (DCNN) to accurately diagnose traumatic pediatric elbow joint diffusion, according to an Oct. 9 study published in the American Journal of Roentgenology.

A new artificial intelligence (AI) algorithm developed by Canadian researchers can detect evidence of cognitive decline in brain MRI scans, genetics and clinical data, and may predict whether findings will lead to Alzheimer’s disease five years before symptoms appear.