Pregnancy

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

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. Still, the features of the signals are not entirely known. Therefore, we present the results on modeling the voltage collected in seawater, in ultrasound ...

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

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 and large 3D data size for GPU implementation, which are far from satisfactory for real-time applications. In this paper, we propose a novel approach ...

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

INTRODUCTION: Ultrasound-guided regional anesthesia (UGRA) involves the acquisition and interpretation of ultrasound images to delineate sonoanatomy. ...

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

Instrumented ultrasonic tracking is used to improve needle localization during ultrasound guidance of minimally invasive percutaneous procedures. Here...

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

Ultrafast ultrasound imaging remains an active area of interest in the ultrasound community due to its ultrahigh frame rates. Recently, a wide variety...

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

In medical imaging, quantitative measurements have shown promise in identifying diseases by classifying normal versus pathological parameters from tis...

Attention-guided deep learning for gestational age prediction using fetal brain MRI.

Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. ...

An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus.

Gestational Diabetes Mellitus (GDM), a common pregnancy complication associated with many maternal and neonatal consequences, is increased in mothers ...

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be re...

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

Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid speci...

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

Placenta localization from obstetric 2-D ultrasound (US) imaging is unattainable for many pregnant women in low-income countries because of a severe s...

Classification of rotator cuff tears in ultrasound images using deep learning models.

Rotator cuff tears (RCTs) are one of the most common shoulder injuries, which are typically diagnosed using relatively expensive and time-consuming di...

A deep learning classification of metacarpophalangeal joints synovial proliferation in rheumatoid arthritis by ultrasound images.

OBJECTIVE: To evaluate if an automatic classification of rheumatoid arthritis (RA) metacarpophalangeal joint conditions in ultrasound images is feasib...

A data-driven ultrasound approach discriminates pathological high grade prostate cancer.

Accurate prostate cancer screening is imperative for reducing the risk of cancer death. Ultrasound imaging, although easy, tends to have low resolutio...

Evaluation of Maturation in Preterm Infants Through an Ensemble Machine Learning Algorithm Using Physiological Signals.

This study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional mat...

Joint segmentation and classification of breast masses based on ultrasound radio-frequency data and convolutional neural networks.

In this paper, we propose a novel deep learning method for joint classification and segmentation of breast masses based on radio-frequency (RF) ultras...

Magnetic Biohybrid Microrobot Multimers Based on Cells for Enhanced Targeted Drug Delivery.

Magnetic micro-/nanorobots have been regarded as a promising platform for targeted drug delivery, and tremendous strategies have been developed in rec...

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients.

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular r...

Deep robust residual network for super-resolution of 2D fetal brain MRI.

Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can ...

Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer.

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast ca...