Pregnancy

Quantifying Valve Regurgitation Using 3-D Doppler Ultrasound Images and Deep Learning.

Accurate quantification of cardiac valve regurgitation jets is fundamental for guiding treatment. Cardiac ultrasound is the preferred diagnostic tool, but current methods for measuring the regurgitant volume (RVol) are limited by low accuracy and high interobserver variability. Following recent research, quantitative estimators of orifice size and RVol based on high frame rate 3-D ultrasound have ...

Deep Learning-Based Framework for Fast and Accurate Acoustic Hologram Generation.

Acoustic holography has been gaining attention for various applications, such as noncontact particle manipulation, noninvasive neuromodulation, and medical imaging. However, only a few studies on how to generate acoustic holograms have been conducted, and even conventional acoustic hologram algorithms show limited performance in the fast and accurate generation of acoustic holograms, thus hinderin...

Ultrasound-enhanced catalytic degradation of simulated dye wastewater using waste printed circuit boards: catalytic performance and artificial neuron network-based simulation.

Recent developments of heterogeneous advanced oxidation for refractory organic contaminants and catalysts made of solid waste have attracted much atte...

Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study.

OBJECTIVES: To establish a breast lesion risk stratification system using ultrasound images to predict breast malignancy and assess Breast Imaging Rep...

Using artificial intelligence to optimize delivery of weight loss treatment: Protocol for an efficacy and cost-effectiveness trial.

Gold standard behavioral weight loss (BWL) is limited by the availability of expert clinicians and high cost of delivery. The artificial intelligence ...

Establishing the Controlled Delivery of VEGF Using a Hydrogel Loaded Soft Robotic Drug Delivery System With the Aim to Prevascularise Implant Site for Islet Transplantation: Erratum.

Intravascular Tracking of Micro-Agents Using Medical Ultrasound: Towards Clinical Applications.

OBJECTIVE: This study demonstrates intravascular micro-agent visualization by utilizing robotic ultrasound-based tracking and visual servoing in clini...

Deep learning for hetero-homo conversion in channel-domain for phase aberration correction in ultrasound imaging.

Echo imaging in ultrasound computed tomography (USCT) using the synthetic aperture technique is performed with the assumption that the speed of sound ...

Unleashing the mechanism among environmental regulation, artificial intelligence, and global value chain leaps: a roadmap toward digital revolution and environmental sustainability.

Strengthening environmental regulation and adhering to the green as well as sustainable development of China's manufacturing industry has become an in...

DeepGA for automatically estimating fetal gestational age through ultrasound imaging.

Accurate estimation of gestational age (GA) is vital for identifying fetal abnormalities. Conventionally, GA is estimated by measuring the morphology ...

Equation learning to identify nano-engineered particle-cell interactions: an interpretable machine learning approach.

Designing nano-engineered particles capable of the delivery of therapeutic and diagnostic agents to a specific target remains a significant challenge....

Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging.

BACKGROUND: Upon the discovery of ovarian cysts, obstetricians, gynecologists, and ultrasound examiners must address the common clinical challenge of ...

Towards machine learning for hydrogel drug delivery systems.

Hydrogel drug delivery system development is complex and laborious, and machine learning (ML) techniques hold great promise in accelerating the proces...

Establishment of a model for predicting the outcome of induced labor in full-term pregnancy based on machine learning algorithm.

To evaluate and establish a prediction model of the outcome of induced labor based on machine learning algorithm. This was a cross-sectional design. T...

Kinematics Calibration and Validation Approach Using Indoor Positioning System for an Omnidirectional Mobile Robot.

Monitoring and tracking issues related to autonomous mobile robots are currently intensively debated in order to ensure a more fluent functionality in...

A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology.

We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmente...

Automatic Segmentation of the Fetus in 3D Magnetic Resonance Images Using Deep Learning: Accurate and Fast Fetal Volume Quantification for Clinical Use.

Magnetic resonance imaging (MRI) provides images for estimating fetal volume and weight, but manual delineations are time consuming. The aims were to ...

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.

Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psych...

3Ddose verification in prostate proton therapy with deep learning-based proton-acoustic imaging.

Dose delivery uncertainty is a major concern in proton therapy, adversely affecting the treatment precision and outcome. Recently, a promising techniq...

Analysis of suction-based gripping strategies in wildlife towards future evolutions of the obstetrical suction cup.

The design of obstetrical suction cups used for vacuum assisted delivery has not substantially evolved through history despite of its inherent limitat...