Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
36085802
Reverberant Shear Wave Elastography (RSWE) is an ultrasound elastography technique that offers great advantages, however, current estimators generate underestimations and time-consuming issues. As well, the involvement of Deep Learning into the medic...
OBJECTIVES: Ultrasound examination has inter-observer and intra-observer variability and a high false-positive rate. The aim of this study was to evaluate the value of the combined use of a deep learning-based computer-aided diagnosis (CAD) system an...
Recently, deep learning using convolutional neural networks (CNNs) has yielded consistent results in image-pattern recognition. This study was aimed at investigating the effectiveness of deep learning using CNNs to differentiate benign and malignant ...
Robotic assistance in minimally invasive surgery offers numerous advantages for both patient and surgeon. However, the lack of force feedback in robotic surgery is a major limitation, and accurately estimating tool-tissue interaction forces remains a...
. Arterial wall stiffness can provide valuable information on the proper function of the cardiovascular system. Ultrasound elasticity imaging techniques have shown great promise as a low-cost and non-invasive tool to enable localized maps of arterial...
Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose To evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepat...
PURPOSE: This study aimed to develop and validate a deep learning model based on two-dimensional (2D) shear wave elastography (SWE) for predicting prognosis in patients with acutely decompensated cirrhosis.
Journal of the mechanical behavior of biomedical materials
37988884
Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of ...
Ultraschall in der Medizin (Stuttgart, Germany : 1980)
38052240
PURPOSE: To investigate the feasibility of deep learning radiomics (DLR) based on multimodal ultrasound to differentiate the primary cancer sites of metastatic cervical lymphadenopathy (CLA).
BACKGROUND: Non-invasive renal fibrosis assessment is critical for tailoring personalized decision-making and managing follow-up in patients with chronic kidney disease (CKD). We aimed to exploit machine learning algorithms using clinical and elastos...