Pregnancy

Use of Machine Learning and Hybrid Closed Loop Insulin Delivery at Diabetes Camps.

Self-co-attention neural network for anatomy segmentation in whole breast ultrasound.

The automated whole breast ultrasound (AWBUS) is a new breast imaging technique that can depict the ...

BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis.

We propose a novel BIRADS-SSDL network that integrates clinically-approved breast lesion characteris...

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study.

BACKGROUND: Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes....

DeepNerve: A New Convolutional Neural Network for the Localization and Segmentation of the Median Nerve in Ultrasound Image Sequences.

Carpal tunnel syndrome commonly occurs in individuals working in occupations that involve use of vib...

Applying cascaded convolutional neural network design further enhances automatic scoring of arthritis disease activity on ultrasound images from rheumatoid arthritis patients.

OBJECTIVES: We have previously shown that neural network technology can be used for scoring arthriti...

Drug delivery: Experiments, mathematical modelling and machine learning.

We address the problem of determining from laboratory experiments the data necessary for a proper mo...

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes.

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction ...

Deep learning promotes B-mode ultrasound screening for focal liver lesions.

Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images.

BACKGROUND AND OBJECTIVE: Deep learning approaches are common in image processing, but often rely on...

Deep learning-based reconstruction of ultrasound images from raw channel data.

PURPOSE: We investigate the feasibility of reconstructing ultrasound images directly from raw channe...

Mechanical heart valves and pregnancy: Issues surrounding anticoagulation. Experience from two obstetric cardiac centres.

BACKGROUND: Pregnant women with mechanical heart valves are at significant risk of obstetric/cardiac...

Computer-aided diagnosis for fetal brain ultrasound images using deep convolutional neural networks.

PURPOSE: Fetal brain abnormalities are some of the most common congenital malformations that may ass...

Breast ultrasound region of interest detection and lesion localisation.

In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region o...

Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images.

The emergence of deep-learning methods in different computer vision tasks has proved to offer increa...

Encoder-decoder CNN models for automatic tracking of tongue contours in real-time ultrasound data.

One application of medical ultrasound imaging is to visualize and characterize human tongue shape an...

Colour Doppler ultrasound of temporal arteries for the diagnosis of giant cell arteritis: a multicentre deep learning study.

OBJECTIVES: Giant cell arteritis (GCA) is the most common systemic vasculitis in adults. In recent y...

Multi-instance Deep Learning of Ultrasound Imaging Data for Pattern Classification of Congenital Abnormalities of the Kidney and Urinary Tract in Children.

OBJECTIVE: To reliably and quickly diagnose children with posterior urethral valves (PUV), we develo...

Supervised machine learning for coronary artery lumen segmentation in intravascular ultrasound images.

Intravascular ultrasound (IVUS) has been widely used to capture cross sectional lumen frames of inne...

Ultrasound Deep Learning for Wall Segmentation and Near-Wall Blood Flow Measurement.

Studies of medical flow imaging have technical limitations for accurate analysis of blood flow dynam...

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries.

BACKGROUND: The process of childbirth is one of the most crucial events in the future health and dev...