The results are in! The American College of Radiology (ACR) and Society for Imaging Informatics in Medicine (SIIM) announced the winners of the groups’ machine learning challenge during SIIM’s Conference on Machine Learning in Medical Imaging in Austin, Texas.

The Mount Sinai Health System in New York City announced Monday, Sept. 23, the creation of its new BioMedical Engineering and Imaging Institute (BMEII).

Convolutional neural networks (CNNs) can accurately identify vertebral fractures (VFs) on x-rays, according to a Sept. 17 study published in Radiology. The method may improve radiologists’ diagnostic ability.

Fujifilm SonoSite and the Allen Institute of Artificial Intelligence Incubator (AI2 Incubator) have announced a new collaboration focused on using AI to interpret ultrasound examinations.  

Subtle Medical has received a grant for up to $1.6 million from the National Institutes of Health (NIH) to develop an AI solution, SubtleGAD, that could reduce the amount of gadolinium used during MRI scans.

AI can predict when patients undergoing radiation treatment for head and neck cancer may lose significant weight or require a feeding tube, according to findings presented at the 2019 annual meeting of the American Society for Radiation Oncology (ASTRO).

A new machine learning approach can predict the negative side effects of radiation treatment in patients with head and neck cancers. The findings, presented at the American Society for Radiation Oncology (ASTRO) annual meeting, can help select patients who might need a more tailored care approach.

RSNA has officially launched a new AI challenge: the RSNA Intracranial Hemorrhage Detection and Classification Challenge. This is the group’s third annual AI challenge.