Machine learning-based CT texture analysis can help with the evaluation of solid renal masses, according to new findings published in Academic Radiology. Could this help reduce the number of patients undergoing unnecessary surgeries?
Numerous deep learning models can detect and classify imaging findings with a performance that rivals human radiologists. However, according to a new study published in the Journal of the American College of Radiology, many of these AI models aren’t nearly as impressive when applied to external data sets.
AI promises to make a titanic impact on radiology, but most of the attention tends to focus on its ability to identify important findings in medical images. What about the technology’s non-interpretive qualities?
Deep learning-based AI models can improve the segmentation of white matter in 18F-FDG PET/CT images, according to a new study published in the Journal of Digital Imaging. This helps radiologists with the early diagnosis of neurodegenerative disease.
Chun Yuan, PhD, has received a two-year, $200,000 grant from the American Heart Association’s Institute for Precision Cardiovascular Medicine for his work on using AI to detect blocked arteries and cardiovascular risk.