Samsung Debuts New Ultrasound Modality Geared Toward Women's Health and Urology
Combining enhanced imaging capabilities, workflow efficiencies and artificial intelligence (AI)-enabled technology to improve the diagnosis and management of health conditions in women, Samsung has launched the V6 ultrasound system.
Advances in artificial intelligence (AI) technology and image sharing tools with Samsung’s new V6 ultrasound platform may facilitate enhanced workflow efficiencies in the management of women’s health conditions.
Samsung noted that key advances with the V6 ultrasound system include:
• one-click fetal growth measurements with the semi-automated BiometryAssist™ feature;
• AI-enabled uterus measurements to help detect uterine-related abnormalities with UterineAssist™; and
• real-time image sharing between sonographers and referring physicians with the SonoSync™ feature.
The V6 ultrasound platform also features a miniER7 endocavity transducer, which gives clinicians a 180-degree field of view, a frequency range between 2 and 12 MHz, and design enhancements that may enhance patient comfort, according to Samsung.
“The V6 was developed with the clinical needs of women’s health and urology specifically in mind,” said Tracy Bury, a women’s health segment leader for Boston Imaging, the United States headquarters of Samsung’s digital radiography and ultrasound business. “We’re proud to offer a new product that is not only accessible to a broad group of clinicians but also has innovative, automated features that can make patient care simpler and more efficient.”
FDA Clears Virtually Helium-Free 1.5T MRI System from Siemens Healthineers
June 26th 2025Offering a cost- and resource-saving DryCool magnet technology, the Magnetom Flow.Ace MRI system reportedly requires 0.7 liters of liquid helium for cooling over the lifetime of the device in contrast to over 1,000 liters commonly utilized with conventional MRI platforms.
Can CT-Based Deep Learning Bolster Prognostic Assessments of Ground-Glass Nodules?
June 19th 2025Emerging research shows that a multiple time-series deep learning model assessment of CT images provides 20 percent higher sensitivity than a delta radiomic model and 56 percent higher sensitivity than a clinical model for prognostic evaluation of ground-glass nodules.
