Pregnancy

Deep Learning for Differentiation of Breast Masses Detected by Screening Ultrasound Elastography.

Recently, deep learning using convolutional neural networks (CNNs) has yielded consistent results in image-pattern recognition. This study was aimed at investigating the effectiveness of deep learning using CNNs to differentiate benign and malignant breast masses identified by elastography on ultrasound screening. A data set of the elastography images of 245 breast masses (146 benign, 99 malignant...

The predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy complicating arrhythmias.

OBJECTIVE: To investigate the predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy (HCM) complicated by arrhythmias.

Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique.

Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise de...

Artificial intelligence breast ultrasound and handheld ultrasound in the BI-RADS categorization of breast lesions: A pilot head to head comparison study in screening program.

BACKGROUND: Artificial intelligence breast ultrasound diagnostic system (AIBUS) has been introduced as an alternative approach for handheld ultrasound...

Development of a Wearable Ultrasound Transducer for Sensing Muscle Activities in Assistive Robotics Applications.

Robotic prostheses and powered exoskeletons are novel assistive robotic devices for modern medicine. Muscle activity sensing plays an important role i...

Current Advances in Computational Lung Ultrasound Imaging: A Review.

In the field of biomedical imaging, ultrasonography has become common practice, and used as an important auxiliary diagnostic tool with unique advanta...

Ultrasound Signal Processing: From Models to Deep Learning.

Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Conventionall...

New frontiers in embryo selection.

Human infertility is a major global public health issue estimated to affect one out of six couples, while the number of assisted reproduction cycles g...

Deep learning of renal scans in children with antenatal hydronephrosis.

INTRODUCTION: Antenatal hydronephrosis (ANH) is one of the most common anomalies identified on prenatal ultrasound, found in up to 4.5% of all pregnan...

Deep Learning-Based Multiclass Brain Tissue Segmentation in Fetal MRIs.

Fetal brain tissue segmentation is essential for quantifying the presence of congenital disorders in the developing fetus. Manual segmentation of feta...

Development of a Machine Learning Model for Sonographic Assessment of Gestational Age.

IMPORTANCE: Fetal ultrasonography is essential for confirmation of gestational age (GA), and accurate GA assessment is important for providing appropr...

A multimodal user interface for touchless control of robotic ultrasound.

PURPOSE: Past research contained the investigation and development of robotic ultrasound. In this context, interfaces which allow for interaction with...

Contrast-Enhanced Ultrasound with Deep Learning with Attention Mechanisms for Predicting Microvascular Invasion in Single Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: Prediction of microvascular invasion (MVI) status of hepatocellular carcinoma (HCC) holds clinical significance for decision...

The Role of a Deep Learning-Based Computer-Aided Diagnosis System and Elastography in Reducing Unnecessary Breast Lesion Biopsies.

OBJECTIVES: Ultrasound examination has inter-observer and intra-observer variability and a high false-positive rate. The aim of this study was to eval...

Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning.

BACKGROUND AND PURPOSE: Fetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning ...

An Embedded Framework for Fully Autonomous Object Manipulation in Robotic-Empowered Assisted Living.

Most of the humanoid social robots currently diffused are designed only for verbal and animated interactions with users, and despite being equipped wi...

Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images.

To develop a deep-learning system for the automatic identification of triple-negative breast cancer (TNBC) solely from ultrasound images. A total of 1...

Ultrasound images-based deep learning radiomics nomogram for preoperative prediction of rearrangement in papillary thyroid carcinoma.

PURPOSE: To create an ultrasound -based deep learning radiomics nomogram (DLRN) for preoperatively predicting the presence of rearrangement among pat...

Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging.

In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection m...

Microrobots for Targeted Delivery and Therapy in Digestive System.

Untethered miniature robots enable targeted delivery and therapy deep inside the gastrointestinal tract in a minimally invasive manner. By combining a...