Researchers utilized five different machine learning approaches to accurately spot lymphedema—a negative side effect of breast cancer treatment—which may help detect it earlier and improve treatment.

A groundbreaking new treatment that involves a magnetic metal “seed” and an MRI scanner could destroy deadly brain tumors in 10 minutes, according to a June 8 article in The Telegraph.

Biomodex, a Paris-based medical device company, announced last week that it plans to open its first 3D human organ printing factory in the U.S. within the year, according to a June 10 article in Newsweek.

First phase of Singapore’s Advanced Medicine Imaging Center provides accurate and timely diagnosis of cancer, plus an education center for medical training and scientific research collaborations.

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.