The American Society of Neuroradiology (ASNR) announced that Peter Chang, MD, a neuroradiology fellow at the University of California San Francisco, has received the Cornelius G. Dyke Memorial Award for his recent research involving deep learning technologies.

Researchers from the New York University Rory Meyers College of Nursing have found machine learning using real-time symptom reports to be accurate in identifying lymphedema early in breast cancer patients. Findings were published in the May 2018 issue of mHealth.

Machine learning algorithms can now identify lymphedema—a chronic side effect of breast cancer treatment—with 94 percent accuracy, New York University researchers reported this month in mHealth.

Utilizing a 3D model in the preoperative planning of osteoplasty for a hip impingement condition can alter the location and extent of the preparation, potentially improving patient outcomes at minimal cost.

Research presented at ASCO 2018 found that using contrast perfusion-weighted MRI enhanced by artificial intelligence (AI) and texture analysis can differentiate between brain tumors according to their mutation status.

“The most striking thing to me as a researcher crafting these attacks was probably how easy they were to carry out," said study lead author Samuel Finlayson, a computer scientist and biomedical informatician at Harvard Medical School in Boston, in an IEEE Spectrum story.

Artificial intelligence (AI) is poised to change transportation with self-driving vehicles, Kimberly Powell, with the Nvidia Corporation, and colleagues believe their work on the subject can also be applied to radiology.

The Society for Imaging Informatics in Medicine’s 2018 annual meeting wrapped up with a keynote address from Curt Langlotz, MD, PhD, with Stanford University, on the rise of artificial intelligence (AI).