Artificial Intelligence

A team from Singapore demonstrated that an object detection convolutional neural network (CNN) could accurately detect and localize fractures on wrist x-rays, according to a Jan. 30 study published in Radiology: Artificial Intelligence. The method may be more verifiable than traditional CNNs.

RSNA has published the first issue of its new online journal, Radiology: Artificial Intelligence.

Researchers from Stanford University in California have published a large, public dataset containing more than 224,000 chest x-rays from more than 65,000 patients to train AI algorithms. The team also announced a competition inviting developers to submit their chest x-ray interpretation models to detect pathologies more accurately than certified radiologists. 

In an effort to destigmatize mental illness and help patients find better treatments, researchers from Virginia Tech’s Fralin Biomedical Research Institute in Roanoke, Virginia trained a machine learning algorithm with brain fMRI scans to diagnosis mental disorders more accurately than standard methods, according to a recent report by The Verge.

“Students rely on us to understand how radiology is incorporating new technology and what the future of the field will look like for them, but many of us are ill prepared to teach the younger generation about this, mostly because we ourselves are not sure,” Allison Grayev, MD, wrote in an editorial published in Academic Radiology. 

A combined deep learning method better detected hemorrhages and identified different subtypes of intracranial hemorrhage than single algorithms used alone, according to a new study published in the Journal of Digital Imaging.

There’s been plenty of hype around the potential of artificial intelligence (AI) to ease radiology workloads. And a new convolutional neural network approach detailed in a Jan. 22 study greatly reduced reporting backlog by accurately triaging chest x-rays in real-time.

The new evidence-based clinical recommendations seek to help labs better use quantitative image analysis (QIA) in HER2 testing for breast cancer.

According to a new market report from Reaction Data, 77 percent of medical imaging professionals believe that machine learning is “important," compared to 65 percent in 2017. Additionally, 59 percent of respondents reported they “understand” machine learning in 2018, compared to 52 percent in 2017.

A newly developed deep learning algorithm more accurately detected cervical precancer than highly experienced physicians and current testing methods, reported authors of a Jan. 10 study published in the Journal of the National Cancer Institute.

With data obtained from fMRI scans and machine learning, National University of Singapore-led (NUS) researchers have a better understanding of the cellular architecture of the brain.

A new artificial intelligence (AI) technology that can identify rare genetic diseases through analyzing an image of a patient's face could help cut diagnosis times for rare diseases and provide more personalized care, according to a new study published online Jan. 7 in Nature Medicine.