As artificial intelligence (AI) and medical imaging continue to transform clinical practice, radiologists-in-training can no longer take a passive role in the march toward this coming change.

A new study from Queen’s University Belfast researchers found the eye may be a critical indicator for Alzheimer’s disease (AD) along with a host of other neurodegenerative diseases.

An MR framework enabling simultaneous multiple parametric T1 and T2 proton density mapping—MR fingerprinting—can identify lesions indicative of a severe neurological condition in patients with a common form of epilepsy—all in under 150 seconds.

Researchers found that using a virtual reality (VR) headset was able to identify brain aneurysms with the same accuracy as matched reference standards, according to a study published in the online July issue of Clinical Neurology and Neurosurgery.

Mechanical engineers and surgeons from the University of Hong Kong have recently developed what could be the world's first neurosurgical robotic system that can perform bilateral stereotactic neurosurgery on a patient inside an MRI machine.

A convolutional neural network (CNN) model performed as well as clinicians in classifying the area of concern in retinal fundus images and provided evidence for why those choices were made—a common problem for artificial intelligence (AI) technology.

MIT researchers have developed an artificial intelligence-(AI) based algorithm that can register three-dimensional (3D) images 1,000 times more quickly than standard medical image registration techniques.

The semi-automated, quantitative 3D approach—joint space mapping (JSM)—detects small changes in joints and is both accurate and precise in measuring joint space compared to traditional two-dimensional (2D) radiography.