Researchers at the University of Arizona Health Sciences are seeking a new and more accurate way to diagnose breast cancer and contribute to improved outcomes for patients worldwide.

For the first time, a sophisticated computer model has been shown to accurately predict two of the most challenging side effects associated with radiation therapy for head and neck cancer. This precision oncology approach has the potential to better identify patients who might benefit from early interventions that may help to prevent significant weight loss after treatment or reduce the need for feeding tube placement. Findings were presented at the 61st Annual Meeting of the American Society for Radiation Oncology (ASTRO), Sept. 15-18, 2019, in Chicago.

Digital medical technology company Brainlab unveiled Loop-X, which it calls the first mobile intraoperative imaging robot, at the 2019 North American Spine Society annual meeting (NASS 2019), Sept. 25-28 in Chicago.

Cardiac magnetic resonance imaging (MRI) analysis can be performed significantly faster with similar precision to experts when using automated machine learning, according to new research. The study was published in Circulation: Cardiovascular Imaging, an American Heart Association journal.

A UCLA-led study has found that using three-dimensional virtual reality (VR) models to prepare for kidney tumor surgeries resulted in substantial improvements, including shorter operating times, less blood loss during surgery and a shorter stay in the hospital afterward.

Siemens Healthineers and national nonprofit HealthyWomen announced results from a new national survey designed to deepen understanding on how to improve women’s experiences with screening mammograms for breast cancer detection. The survey, which included responses from women over age 40 (all of whom fall under the recommended screening guidelines), revealed that while 61 percent of women said mammograms are necessary, 52 percent found them uncomfortable and 25 percent regarded them as painful.

Fujifilm SonoSite Inc. and the Allen Institute of Artificial Intelligence (AI2) Incubator, builder of AI-first startups, announced a collaboration to interpret ultrasound images with AI, enabling new ultrasound applications and enhanced accuracy. Fujifilm SonoSite has enlisted assistance from the AI2 Incubator to deploy deep learning models on portable ultrasound products. Together, the AI2 Incubator and Fujifilm SonoSite will work to improve image analysis, allowing for the interpretation of a much wider range of ultrasound scenarios.