Pregnancy

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks.

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolut...

Automation and artificial intelligence in radiation therapy treatment planning.

Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving workflows and increasing efficiency in radiation oncology departments. Currently, AI technology is advancing at an exponential rate, as are its applications in radiation oncology. This commentary highlights the way AI has begun to impact radiation th...

Development and validation of an artificial intelligence assisted prenatal ultrasonography screening system for trainees.

OBJECTIVE: Fetal anomaly screening via ultrasonography, which involves capturing and interpreting standard views, is highly challenging for inexperien...

Improved precise guidewire delivery of a cardiovascular interventional surgery robot based on admittance control.

PURPOSE: The development of cardiovascular interventional surgery robots can realize master-slave interventional operations, which will effectively so...

Deep learning for fast denoising filtering in ultrasound localization microscopy.

Addition of a denoising filter step in ultrasound localization microscopy (ULM) has been shown to effectively reduce the error localizations of microb...

Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme?

BACKGROUND: Artificial intelligence (AI) has the potential to improve prenatal detection of congenital heart disease. We analysed the performance of t...

Approaches and Limitations of Machine Learning for Synthetic Ultrasound Generation: A Scoping Review.

This scoping review examines the emerging field of synthetic ultrasound generation using machine learning (ML) models in radiology. Nineteen studies w...

An intelligent adverse delivery outcomes prediction model based on the fusion of multiple obstetric clinical data.

Adverse delivery outcomes is a major re-productive health problem that affects the physical and mental health of pregnant women. Obviously, obstetric ...

A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques.

The despeckling of ultrasound images contributes to the enhancement of image quality and facilitates precise treatment of conditions such as tumor can...

Analysis of Prospective Genetic Indicators for Prenatal Exposure to Arsenic in Newborn Cord Blood of Using Machine Learning.

Using a machine learning methods, we aim to find biological effect biomarkers of prenatal arsenic exposure in newborn cord blood. From the Gene Expres...

Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design.

Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting...

Diagnosis of diabetes mellitus using high frequency ultrasound and convolutional neural network.

The incidence of diabetes mellitus has been increasing, prompting the search for non-invasive diagnostic methods. Although current methods exist, thes...

Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods.

This study aimed to predict Blackbelly sheep carcass tissue composition using ultrasound measurements and machine learning models. The models evaluate...

Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy.

OBJECTIVE: The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber...

A deep sift convolutional neural networks for total brain volume estimation from 3D ultrasound images.

Preterm infants are a highly vulnerable population. The total brain volume (TBV) of these infants can be accurately estimated by brain ultrasound (US)...

Gray-to-color image conversion in the classification of breast lesions on ultrasound using pre-trained deep neural networks.

Breast ultrasound (BUS) image classification in benign and malignant classes is often based on pre-trained convolutional neural networks (CNNs) to cop...

Serum myosin-binding protein c levels: a new marker for exclusion of preterm birth?

BACKGROUND/AIM: To evaluate whether there is a relationship between serum myosin-binding protein C (MyBP-C) levels measured in the first trimester and...

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line arti...

Automated stereological image analysis approach of the human placenta: Surface areas and vascularization.

Detecting and quantifying surface densities of placental villi and their vasculature adds important information on the development of the placenta und...

Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study.

BACKGROUND: This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions...