“Our findings show that AI-assistance can effectively improve contouring accuracy and reduce intra- and interobserver variation and contouring time, which could have a positive impact on tumor control and patient survival,” wrote authors of a recent study published in Radiology.

To improve transparency, black box algorithms are increasingly being built with functions that explain their diagnostic findings. But a recent NPR report examined how this isn’t always effective, and why a different approach to creating algorithms may be the answer.

Going by a small study published online March 22 in European Radiology, biplanar digital radiographs of the hip joint reconstructed in 3D as well as 2D are sufficiently comparable on coverage assessments with similarly rendered CT scans to justify the reduction in radiation dose as well as in, presumably, the cost of image acquisition.

Research presented at this year’s Society of Interventional Radiology conference in Austin, Texas, suggests virtual reality (VR) could be useful to interventional radiologists, improving treatments using real-time 3D images from inside a patient’s vessels.

It’s well established that widely used CPAP devices help give a good night’s rest to people with obstructive sleep apnea. Now an MRI-based study has shown the breathing assistance provided by continuous positive airway pressure also increases blood flow to, and blood volume in, the brain. 

Google’s AI research group has shown that deep-learning algorithms can fine-tune ophthalmologists’ diagnosis of diabetic retinopathy on retinal fundus photographs, according to a study slated for publication in Ophthalmology. In the study, the physicians using the algorithm bested both AI alone and unassisted physicians on accuracy.

Researchers can more accurately and comprehensively study the brain with a novel concept known as diattenuation imaging (DI)—a neuroimaging method that allows scientists to measure the polarization-dependent attenuation of light throughout different parts of the brain—according to a study published in Scientific Reports.

When manually corrected by radiologists, an AI system for automatically detecting and segmenting colorectal metastases in the liver can improve interpretative efficiency, according to a study published online March 13 in Radiology: Artificial Intelligence.