Pregnancy

Evaluation of the predictive ability of ultrasound-based assessment of breast cancer using BI-RADS natural language reporting against commercial transcriptome-based tests.

PURPOSE: The objective of this study was to assess the classification capability of Breast Imaging Reporting and Data System (BI-RADS) ultrasound feature descriptors targeting established commercial transcriptomic gene signatures that guide management of breast cancer.

Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks.

For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essentia...

Real-time prediction of tumor motion using a dynamic neural network.

Radiation dose delivery into the thoracic and abdomen cavities during radiotherapy treatment is a challenging task as respiratory motion leads to the ...

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning.

Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contras...

The Digital Health Revolution and People with Disabilities: Perspective from the United States.

This article serves as the introduction to this special issue on Mobile Health and Mobile Rehabilitation for People with Disabilities. Social, technol...

Machine Learning in Fetal Cardiology: What to Expect.

In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised card...

Deep Learning-Based Super-resolution Ultrasound Speckle Tracking Velocimetry.

Deep ultrasound localization microscopy (deep-ULM) allows sub-wavelength resolution imaging with deep learning. However, the injection of contrast age...

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.

OBJECTIVES: We aimed to establish and validate an artificial intelligence-based radiomics strategy for predicting personalized responses of hepatocell...

Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation.

BACKGROUND AND OBJECTIVE: The measurement of carotid intima media thickness (CIMT) in ultrasound images can be used to detect the presence of atherosc...

Self-assembled natural phytochemicals for synergistically antibacterial application from the enlightenment of traditional Chinese medicine combination.

The application of nanotechnology for antimicrobial delivery has capacity to improve antibacterial efficacy. Currently, the usage of various inorganic...

A novel transcranial ultrasound imaging method with diverging wave transmission and deep learning approach.

Real time brain transcranial ultrasound imaging is extremely intriguing because of its numerous applications. However, the skull causes phase distorti...

Breast tumor classification through learning from noisy labeled ultrasound images.

PURPOSE: To train deep learning models to differentiate benign and malignant breast tumors in ultrasound images, we need to collect many training samp...

DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network.

BACKGROUND: Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non...

Predicting the serum digoxin concentrations of infants in the neonatal intensive care unit through an artificial neural network.

BACKGROUND: Given its narrow therapeutic range, digoxin's pharmacokinetic parameters in infants are difficult to predict due to variation in birth wei...

Diagnosis of Parathyroid Adenomas with New Ultrasound Imaging Modalities.

Ultrasound technology is becoming an integral part of diagnosing parathyroid adenomas. Careful ultrasound evaluation with b-mode, shear wave elastogra...

Deep Convolutional Neural Network-Based Automatic Classification of Neonatal Hip Ultrasound Images: A Novel Data Augmentation Approach with Speckle Noise Reduction.

Neonatal hip ultrasound imaging has been widely used for a few decades in the diagnosis of developmental dysplasia of the hip (DDH). Graf's method of ...

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound.

Recent advances in fetal imaging open the door to enhanced detection of fetal disorders and computer-assisted surgical planning. However, precise segm...

Are All Deep Learning Architectures Alike for Point-of-Care Ultrasound?: Evidence From a Cardiac Image Classification Model Suggests Otherwise.

OBJECTIVES: Little is known about optimal deep learning (DL) approaches for point-of-care ultrasound (POCUS) applications. We compared 6 popular DL ar...

Effect of Timing of Postovulatory Insemination Relative to Human Chorionic Gonadotropin/Buserelin Treatment With 1 Straw of Frozen-Thawed Semen on Mare Fertility.

The reproductive management of mares for frozen semen artificial insemination (AI) can be costly and labor intensive. Predicting the exact time of ovu...

Obstetric Hemorrhage Outcomes by Intrapartum Risk Stratification at a Single Tertiary Care Center.

Introduction Postpartum hemorrhage is a leading cause of maternal mortality worldwide. Performance of a postpartum hemorrhage risk assessment prior to...