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 models can be trained to predict outcomes in meningioma patients, according to new research published in npjDigital Medicine. The study’s authors even developed a free smartphone app so others can explore their work.
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.
Deep learning-based reconstruction (DLR) can reduce the radiation dose associated with low-dose chest and abdominal CT scans without sacrificing image quality, according to a new study published in the American Journal of Roentgenology.