September 28, 2010 – Physicians can now determine whether a patient needs to have cardiac radionuclide imaging (RNI) by using a new smartphone app. The app, which is available for free on iPhone, BlackBerry and Android phones, gives physicians access to published criteria used to assess the need for RNI. The "Appropriate Use Criteria (AUC) for Cardiac Radionuclide Imaging" app, by Astellas, describes 67 individual patient indications, eight indication categories and five algorithms. All indications were adapted from the AUC published in 2009 in Circulation. The app guides physicians step by step through the criteria to evaluate patient history, clinical factors, and other important information to receive an instant appropriate use score for RNI. By entering information, physicians will receive a rating on a scale of 1-9 suggesting if the patient indication is appropriate (7-9), uncertain (4-6), or inappropriate (1-3) for RNI. Ratings can be viewed either by indication/category or patient algorithm. "Astellas recognizes physicians are increasingly using smartphones and apps in the healthcare setting to access information," said Robert C. Hendel, M.D., FACC, FAHA, FASNC, and chair of the Cardiac Radionuclide Imaging Writing Group that developed the 2009 AUC for RNI. "We partnered with them to develop this accurate, timely assessment tool for use on smartphones and hope this app will promote awareness and use of the AUC for RNI published in 2009." According to the annual "Taking the Pulse" study of physicians and healthcare technology released in April 2010, 72 percent of physicians use smartphones personally and professionally. That number is expected to jump to 81 percent in 2012. For more information: www.astellasapps.com
Smartphone App Helps Physicians Assess the Need for Radionuclide Imaging
Jennifer N. A. Silva, M.D., a pediatric cardiologist at Washington University School of Medicine in Saint Louis, Mo., describes “mixed reality” at ACC19 Future Hub.
Collage depicts broad applications in machine learning or deep learning (DL) that can be applied to advanced medical imaging technologies. Size of the liver and its fat fraction — 22 percent — (top middle in collage) can be quantified automatically using an algorithm developed by Dr. Albert Hsiao and his team at the University of California San Diego. This and other information that might be mined by DL algorithms from CT and MR images could help personalize patients’ treatment. Collage provided by Albert Hsiao