AI Medical Compendium Topic:
Ultrasonography

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Modeling the Voltage Produced by Ultrasound in Seawater by Stochastic and Artificial Intelligence Methods.

Sensors (Basel, Switzerland)
Experiments have proved that an electrical signal appears in the ultrasonic cavitation field; its properties are influenced by the ultrasound frequency, the liquid type, and liquid characteristics such as density, viscosity, and surface tension. Stil...

Weakly-supervised learning for catheter segmentation in 3D frustum ultrasound.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate and efficient catheter segmentation in 3D ultrasound (US) is essential for ultrasound-guided cardiac interventions. State-of-the-art segmentation algorithms, based on convolutional neural networks (CNNs), suffer from high computational cost ...

Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia.

Regional anesthesia and pain medicine
INTRODUCTION: Ultrasound-guided regional anesthesia (UGRA) involves the acquisition and interpretation of ultrasound images to delineate sonoanatomy. This study explores the utility of a novel artificial intelligence (AI) device designed to assist in...

Disease-Specific Imaging Utilizing Support Vector Machine Classification of H-Scan Parameters: Assessment of Steatosis in a Rat Model.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
In medical imaging, quantitative measurements have shown promise in identifying diseases by classifying normal versus pathological parameters from tissues. The support vector machine (SVM) has shown promise as a supervised classification algorithm an...

Complex Convolutional Neural Networks for Ultrafast Ultrasound Imaging Reconstruction From In-Phase/Quadrature Signal.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrafast ultrasound imaging remains an active area of interest in the ultrasound community due to its ultrahigh frame rates. Recently, a wide variety of studies based on deep learning have sought to improve ultrafast ultrasound imaging. Most of thes...

Deep Learning for Instrumented Ultrasonic Tracking: From Synthetic Training Data to In Vivo Application.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Instrumented ultrasonic tracking is used to improve needle localization during ultrasound guidance of minimally invasive percutaneous procedures. Here, it is implemented with transmitted ultrasound pulses from a clinical ultrasound imaging probe, whi...

Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithms.

Pediatric radiology
BACKGROUND: Germinal matrix hemorrhage-intraventricular hemorrhage is among the most common intracranial complications in premature infants. Early detection is important to guide clinical management for improved patient prognosis.

Multi-channel convolutional neural network architectures for thyroid cancer detection.

PloS one
Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations du...

Three-Dimensional Carpal Arch Morphology Using Robot-Assisted Ultrasonography.

IEEE transactions on bio-medical engineering
OBJECTIVE: The morphology of the carpal arch implicates the available space for the median nerve within the carpal tunnel. The purposes of this study were to 1) reconstruct the three-dimensional (3D) carpal arch by robot-assisted ultrasonography with...

Automatic Placenta Localization From Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound Acquisition Protocol and Deep Learning.

Ultrasound in medicine & biology
Placenta localization from obstetric 2-D ultrasound (US) imaging is unattainable for many pregnant women in low-income countries because of a severe shortage of trained sonographers. To address this problem, we present a method to automatically detec...