Researchers have created an artificial intelligence (AI) algorithm capable of reading CT scans to predict how patients will respond to a specific immunotherapy, according to a Lancet Oncology study.

“We found no evidence of intracranial gadolinium deposition on brain MR images in patients who had intraarticular gadolinium administered for MR arthrography and no other GBCA exposure,” wrote lead author Lauren Ladd, MD, and a radiologist at the Indiana University School of Medicine.

The Radiological Society of North America (RSNA) kicked off its second annual machine learning challenge on Aug. 27, inviting teams to create an algorithm capable of identifying and localizing pneumonia on chest x-rays.

Researchers from Duke University have created a handheld probe capable of capturing images of photoreceptors in the eyes of infants—potentially aiding early diagnoses of brain-related diseases and trauma.

A team of engineers has developed an artificial intelligence (AI) system that can find lung cancer on CT scans with 95 percent accuracy.

Researchers at the University of Central Florida’s Center for Research in Computer Vision have created an artificial intelligence (AI) algorithm that can detect specks of lung cancer in CT scans with 95 percent accuracy.

Virtual Brain Segmenter (VBS), which automatically processes medical imaging data such as MRIs through segmentation, could increase the efficiency of brain scan analysis, according to a USC news release from Aug. 21.

Nanoscientists at Rice University in Houston have created a method to pack iron into nanoparticles to create MRI contrast agents that outperform gadolinium-based agents, according to research published in ACS Nano.