Artificial Intelligence

Imperial College London announced its plans to open the London Medical Imaging and Artificial Intelligence Center for Value-Based Healthcare thanks to £10 million ($13 million) in recently awarded funds, according to a university news release published Nov. 6.

A new artificial intelligence (AI) algorithm trained on 18-F-fluorodeoxyglucose PET (18FDG-PET) scans can predict the occurrence of Alzheimer’s disease at least six years before diagnosis with 100 percent sensitivity, according to research published Nov. 6 in Radiology.

“This system has made everything much more transparent in that we can see everyone in every area of the hospital and it makes every area much more transparent because we can see what’s happening,” interventional radiologist Ellen Francesconi, MD, told HealthImaging.

Using a breast MRI tumor dataset, researchers found a deep learning convolutional neural network (CNN) approach could be trained to predict responses to chemotherapy prior to its initiation, according to a recent Journal of Digital Imaging study.

The American College of Radiology Data Science Institute (ACR DSI) recently released a series of standardized artificial intelligence (AI) use cases to help advance imaging in AI. Down the road, they could help create an “AI ecosystem” for radiology, wrote Bibb Allen, MD, chief medical officer of the ACR DSI, in a recent Journal of the American College of Radiology editorial.

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.