The purpose is to develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis. We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI.

Herzog M, Arsova M, Matthes K et al.

Since handheld ultrasound devices are becoming increasingly ubiquitous, objective criteria to determine image quality are needed. We therefore conducted a comparison of objective quality measures and clinical performance. A comparison of handheld devices (Butterfly IQ+, Clarius HD, Clarius HD3, Philips Lumify, GE VScan Air) and workstations (GE Logiq E10, Toshiba Aplio 500) was performed using a phantom. As a comparison, clinical investigations were performed by two experienced ultrasonographers by measuring the resolution of anatomical structures in the liver, pancreas, and intestine in ten subjects.