Medtronic and Viz.ai, a growing leader in artificial intelligence, are collaborating on a new software to automatically alert specialists when a stroke is identified during a CT scan.

A deep learning model that simulates a clinician’s diagnostic process can accurately diagnose Alzheimer’s disease from cognitively normal patients, according to a study published July 16 in Neurocomputing.

“The CXR-risk score took as input the radiograph only. This was intended to prove a point—that a CNN can extract prognostic information embedded in the image, without any other demographic or clinical information,” wrote authors of a new study published in JAMA Network Open.

“The differences that we observed indicate the strong possibility that there are sex differences in the structural and functional connections in the brain, which may contribute to women’s increased risk for Alzheimer’s,” said Sepideh Shokouhi, at the Alzheimer’s Association International Conference in Los Angeles.

A newly created three-dimensional (3D) neural network can improve the detection of pulmonary nodules on CT scans, according to a study published July 12 in PLOS ONE. 

Gadolinium-based contrast agents (GBCAs) are central to MRI exams, providing critical information unavailable with other modalities. However, some patients experience acute adverse reactions, and investigators of a recent study published in Radiology set out to get a clearer picture of these events.

Can a virtual world instill the same level of knowledge in radiology students as a traditional face-to-face classroom approach? Researchers of a new study published in the American Journal of Roentgenology think so.

The algorithm improved the specificity of thyroid biopsy recommendations, beating seven of nine radiologists. With more research, the algorithm could help in the decision-making process for assessing thyroid nodules.