Aunt Minnie (6/24) reports that using artificial intelligence (AI) to analyze breast ultrasound lesions—particularly for lesions classified as BI-RADS 2, 3, and 4A—reduced interpretation time by 49% at a healthcare center in Missouri, according to a presentation at the recent Society for Imaging Informatics in Medicine's (SIIM) virtual meeting.

Aunt Minnie (6/23) reports that digital breast tomosynthesis (DBT) exposed women to significantly less radiation than full-field digital mammography (FFDM) in a study of 200 patients published in the Journal of Medical Radiation Sciences.

Health Imaging (6/19) reports that a team of German doctors retrospectively analyzed a new needle-based detector system—which reduces light scattering and enhances x-ray absorption—in 360 mammograms for their study published in the European Journal of Radiology, which found that the needle-based imaging plate system reduced required dosage by nearly 30% and helped experts view and score key areas on mammograms.

Aunt Minnie (6/15) reports on a study from European Radiology which states that lesion classification scheme named after breast MRI pioneer Dr. Werner Kaiser may help prevent anywhere from 45% to 72.5% of unnecessary breast biopsies in high-risk patients, Austrian researchers have found.

Health Imaging (6/12) reports on a study from the Journal of Digital Imaging which states that A new artificial intelligence-based computer-aided detection model can accurately predict breast cancer based on ultrasound images.