Image IQ: Ultrasound Screening of Dense Breasts

In comparison to native 64-mT MRI, the deep learning generative model LowGAN offered enhanced white matter lesion conspicuity and image quality in a study involving patients with multiple sclerosis.

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists.

The additional FDA 510(k) clearance for the AI-powered cmAngio platform covers use of the software for GE HealthCare mammography systems.

In the second episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss recent studies published by the Journal of the American Medical Association (JAMA) that suggested moving to more of a risk-adapted model for mammography screening.

Demonstrating no significant difference with radiologist detection of clinically significant prostate cancer (csPCa), a biparametric MRI-based AI model provided an 88.4 percent sensitivity rate in a recent study.

In a new study involving over 120 women, nearly two-thirds of whom had a family history of breast cancer, ultrafast MRI findings revealed a 5 percent increase in malignancy risk for each second increase in the difference between lesion and background parenchymal enhancement (BPE) time to enhancement (TTE).