Pregnancy

Patient selection for proton therapy: a radiobiological fuzzy Markov model incorporating robust plan analysis.

While proton therapy can offer increased sparing of healthy tissue compared with X-ray therapy, it can be difficult to predict whether a benefit can be expected for an individual patient. Predictive modelling may aid in this respect. However, the predictions of these models can be affected by uncertainties in radiobiological model parameters and in planned dose. The aim of this work is to present ...

Many-isocenter optimization for robotic radiotherapy.

Despite significant dosimetric gains, clinical implementation of the 4π non-coplanar radiotherapy on the widely available C-arm gantry system is hindered by limited clearance, and the need to perform complex coordinated gantry and couch motion. A robotic radiotherapy platform would be conducive to such treatment but a new conflict between field size and MLC modulation resolution needs to be manage...

End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising.

OBJECTIVE: Non-invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. Howeve...

Automatic detection of brachytherapy seeds in 3D ultrasound images using a convolutional neural network.

A novel approach for automatic localization of brachytherapy seeds in 3D transrectal ultrasound (TRUS) images, using machine learning based algorithm,...

Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks.

Thyroid cancer is a disease in which the first symptom is a nodule in the thyroid region of the neck. It is one of the cancers with the highest incide...

An artificial neural network to model response of a radiotherapy beam monitoring system.

PURPOSE: The integral quality monitor (IQM) is a real-time radiotherapy beam monitoring system, which consists of a spatially sensitive large-area ion...

Prediction of vaginal birth after cesarean deliveries using machine learning.

BACKGROUND: Efforts to reduce cesarean delivery rates to 12-15% have been undertaken worldwide. Special focus has been directed towards parturients wh...

Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network.

PURPOSES: Multileaf collimator (MLC) positional accuracy during dynamic intensity modulation radiotherapy (IMRT) delivery is crucial for safe and accu...

Neural-network-based Motion Tracking for Breast Ultrasound Strain Elastography: An Initial Assessment of Performance and Feasibility.

Accurate tracking of tissue motion is critically important for several ultrasound elastography methods. In this study, we investigate the feasibility ...

Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks.

The objective is to test automated estimation of active and passive skeletal muscle states using ultrasonic imaging. Current technology (electromyogr...

Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network.

BACKGROUND AND OBJECTIVES: Automated breast ultrasound (ABUS) is a widely used screening modality for breast cancer detection and diagnosis. In this s...

Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks.

Breast ultrasound and computer aided diagnosis (CAD) has been used to classify tumors into benignancy or malignancy. However, conventional CAD softwar...

Segmentation of breast ultrasound image with semantic classification of superpixels.

Breast cancer is a great threat to females. Ultrasound imaging has been applied extensively in diagnosis of breast cancer. Due to the poor image quali...

Nodular Thyroid Disease in the Era of Precision Medicine.

Management of thyroid nodules in the era of precision medicine is continuously changing. Neck ultrasound plays a pivotal role in the diagnosis and sev...

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review.

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however...

Using Deep Learning in Ultrasound Imaging of Bicipital Peritendinous Effusion to Grade Inflammation Severity.

Inflammation of the long head of the biceps tendon is a common cause of shoulder pain. Bicipital peritendinous effusion (BPE) is the most common bicep...

First-in-human evaluation of a hand-held automated venipuncture device for rapid venous blood draws.

Obtaining venous access for blood sampling or intravenous (IV) fluid delivery is an essential first step in patient care. However, success rates rely ...

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.

OBJECTIVES: To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound im...

MNT-DeepSL: Median nerve tracking from carpal tunnel ultrasound images with deep similarity learning and analysis on continuous wrist motions.

Carpal tunnel syndrome (CTS) is a clinical disease that caused by the compression of median nerve within carpal tunnel. Traditional examining for CTS ...

SVM-Enabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems.

In recent years, there are several cost-effective intelligent sensing systems such as ultrasound imaging systems for visualizing the internal body str...