Pregnancy

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 increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning ...

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 and motion in real-time to study healthy or impaired speech production. Due to the low-contrast characteristic and noisy nature of ultrasound images, it requires knowledge about the tongue structure and ultrasound data interpretation for users to recognize tongue locations and gestures easily. Moreove...

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 years, colour Doppler ultrasound of the temporal ar...

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 inner wall of coronary arteries. This kind of medical ...

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 dynamics and vessel wall interaction at arteries. We pr...

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 development of the offspring. The vulnerability of pa...

Generative approach for data augmentation for deep learning-based bone surface segmentation from ultrasound images.

PURPOSE: Precise localization of cystic bone lesions is crucial for osteolytic bone tumor surgery. Recently, there is a move toward ultrasound imaging...

Deep learning-based classification of rectal fecal retention and analysis of fecal properties using ultrasound images in older adult patients.

AIM: The present study aimed to analyze the use of machine learning in ultrasound (US)-based fecal retention assessment.

Alveolar Bone Segmentation in Intraoral Ultrasonographs with Machine Learning.

The use of intraoral ultrasound imaging has received great attention recently due to the benefits of being a portable and low-cost imaging solution fo...

A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.

The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome is fraught with issues of re...

Autonomous Biohybrid Urchin-Like Microperforator for Intracellular Payload Delivery.

A magnetic urchin-like microswimmer based on sunflower pollen grain (SPG) that can pierce the cancer cell membrane and actively deliver therapeutic dr...

Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy.

PURPOSE: Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks ...

Cardiac VFM visualization and analysis based on YOLO deep learning model and modified 2D continuity equation.

In order to realize the visual analysis of cardiac fluid motion, according to the characteristics of cardiac flow field ultrasound image, a method for...

Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment.

The objective of this study was to assess the impact of artificial intelligence (AI)-based decision support (DS) on breast ultrasound (US) lesion ass...

Segmentation of finger tendon and synovial sheath in ultrasound image using deep convolutional neural network.

BACKGROUND: Trigger finger is a common hand disease, which is caused by a mismatch in diameter between the tendon and the pulley. Ultrasound images ar...

Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging.

Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures ...

Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk.

Terminal duct lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more li...

FF-QuantSC: accurate quantification of fetal fraction by a neural network model.

BACKGROUND: Noninvasive prenatal testing (NIPT) is one of the most commonly employed clinical measures for screening of fetal aneuploidy. Fetal Fracti...

Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images.

PURPOSE: We aimed to propose a highly automatic and objective model named deep learning Radiomics of thyroid (DLRT) for the differential diagnosis of ...

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.

The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the ...