GE settles SonoSite lawsuits

Researchers found that a deep convolutional neural network, incorporating B-mode ultrasound and shear wave elastography data, had nearly an 88 percent sensitivity rate for predicting neoadjuvant chemotherapy response in patients with breast cancer.

In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.

Emerging breast ultrasound research showed the use of computer-aided diagnosis (CAD), powered by deep learning, led to 24 percent and 36.9 percent improvements in accuracy and specificity, respectively, in the use of BI-RADS classifications by radiologists without breast ultrasound expertise.

Dr. Laurence Klotz discusses micro-ultrasound as a faster, less invasive, more cost-effective screening strategy compared to mpMRI in this podcast.

Adjunctive use of an artificial intelligence (AI) software demonstrated nearly equivalent sensitivity and over 28 percent higher accuracy in comparison to radiologist assessment of breast ultrasound images for breast lesions, according to new research presented at the recent Society of Breast Imaging (SBI) conference.

The artificial intelligence (AI)-powered EchoGo Amyloidosis can reportedly detect cardiac amyloidosis by assessing a single echocardiogram.