Pregnancy

Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia.

BACKGROUND: We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia.

Nondestructive Detection of Targeted Microbubbles Using Dual-Mode Data and Deep Learning for Real-Time Ultrasound Molecular Imaging.

Ultrasound molecular imaging (UMI) is enabled by targeted microbubbles (MBs), which are highly reflective ultrasound contrast agents that bind to specific biomarkers. Distinguishing between adherent MBs and background signals can be challenging in vivo. The preferred preclinical technique is differential targeted enhancement (DTE), wherein a strong acoustic pulse is used to destroy MBs to verify t...

Deep Learning for Ultrasound Localization Microscopy.

By localizing microbubbles (MBs) in the vasculature, ultrasound localization microscopy (ULM) has recently been proposed, which greatly improves the s...

Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.

PURPOSE: Needle-based procedures for diagnosing and treating prostate cancer, such as biopsy and brachytherapy, have incorporated three-dimensional (3...

Diagnosis of Thyroid Nodule with New Ultrasound Imaging Modalities.

B-mode ultrasound (US) technology is an integral part of diagnosing and assessing risk stratification of thyroid nodules (TNs). The addition of shear...

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence.

Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis me...

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been...

Creation and Testing of a Deep Learning Algorithm to Automatically Identify and Label Vessels, Nerves, Tendons, and Bones on Cross-sectional Point-of-Care Ultrasound Scans for Peripheral Intravenous Catheter Placement by Novices.

OBJECTIVES: We sought to create a deep learning (DL) algorithm to identify vessels, bones, nerves, and tendons on transverse upper extremity (UE) ultr...

Machine Learning Diagnostic Modeling for Classifying Fibromyalgia Using B-mode Ultrasound Images.

Fibromyalgia (FM) diagnosis remains a challenge for clinicians due to a lack of objective diagnostic tools. One proposed solution is the use of quanti...

AIBx, Artificial Intelligence Model to Risk Stratify Thyroid Nodules.

Current classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivi...

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning.

Assessing the viability of a blastosyst is still empirical and non-reproducible nowadays. We developed an algorithm based on artificial vision and mac...

Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound.

In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and the contrast-to-nois...

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers.

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic-ischemic encephalop...

Robotic fetal ultrasonography platform with a passive scan mechanism.

PURPOSE: The shortage of obstetricians and gynecologists has intensified in developed countries. Our long-term goal is to develop a robotic prenatal c...

Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat with Radiofrequency Ultrasound Data Using One-dimensional Convolutional Neural Networks.

Background Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might expl...

Detecting vulnerable plaque with vulnerability index based on convolutional neural networks.

Plaque rupture and subsequent thrombosis are major processes of acute cardiovascular events. The Vulnerability Index is a very important indicator of ...

Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage.

Preterm birth, defined as a delivery before 37 weeks' gestation, continues to affect 8-15% of all pregnancies and is associated with significant neona...

Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.

To assist radiologists in breast cancer classification in automated breast ultrasound (ABUS) imaging, we propose a computer-aided diagnosis based on a...

Hierarchical Class Incremental Learning of Anatomical Structures in Fetal Echocardiography Videos.

This paper proposes an ultrasound video interpretation algorithm that enables novel classes or instances to be added over time, without significantly ...

Channel Attention Module With Multiscale Grid Average Pooling for Breast Cancer Segmentation in an Ultrasound Image.

Breast cancer accounts for the second-largest number of deaths in women around the world, and more than 8% of women will suffer from the disease in th...