Imaging Informatics

Radiology has continuously been on the forefront of adopting new technologies. But at one institution, it took a bit of training and exposure to existing interactive multimedia reporting features before radiologists were willing to adopt it into clinical practice.

A deep learning algorithm developed by researchers at the Mayo Clinic in Rochester, Minnesota, segmented abdominal CT images to determine body composition similarly to, and at times, better than trained radiologists.

Imaging orders sent via electronic health records (EHRs) have less complete—and consequently less reliable—patient information than those found in physician notes on the same patient in the same EHR, according to research published online Dec. 5 in the Journal of the American Medical Informatics Association.

Massachusetts General Hospital (MGH) in Boston is diving straight into the hype of blockchain by collaborating with Korean blockchain startup MediBloc to improve the health system’s patient data sharing and storage capabilities, according to a report published Dec. 5 by CoinDesk.

Radiology has the highest use of telemedicine for patient interactions than any other medical specialty, according to results from a nationally representative survey published in the December issue of Health Affairs by the American Medical Association (AMA).

By using radiomics, Chinese researchers found that the diagnostic performance of mammography could improve and offer complementary information to radiologists regarding benign and malignant breast tumors, as reported in the Journal of the American College of Radiology on Dec. 5.

Artificial intelligence (AI) and big data can help radiologists provide better care while reducing costs, but a majority of institutions lack the infrastructure to optimally consume and utilize these technologies, said Paul Chang, MD, of the University of Chicago, during RSNA's 2018 Annual Meeting.

Researchers from Stanford University have determined that convolutional neural networks (CNNs) trained with just 20,000 labeled images can accurately classify chest x-rays as either normal or abnormal, according to a new study published Nov. 13 in Radiology.

A decision support tool can help physicians better diagnose bladder cancer treatment response on CT, according to an Nov. 10 study published in Academic Radiology.

Researchers from the Medical University of Vienna in Austria provided new guidance for selecting optimizing features from 18F-FDG-PET/CT studies—demonstrating feature variations can be minimized for selected image parameters and imaging systems, in a new study published Nov. 2 in the Journal of Nuclear Medicine.

Medical images and articles found on Wikipedia may help patients better understand their radiology reports, according to a new study published in the Journal of Digital Imaging.

In an increasingly technology-driven world, more hospitals are introducing patient portal systems. But when it comes to delivering imaging results to patients, instant access isn’t always best, wrote authors of a new study published in Radiology.