Pregnancy

BentRay-NeRF: Bent-Ray Neural Radiance Fields for Robust Speed-of-Sound Imaging in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) is a promising technique for breast cancer detection because of its quantitative imaging capability of the speed of sound (SOS) of soft tissues and the fact that malignant breast lesions often have a higher SOS compared to healthy tissues in the human breast. Compared to waveform inversion-based USCT, bent-ray tracing USCT is relatively less computationally ex...

Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images.

Ultrasound (US) is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its noninvasiveness and availability. Deep learning methods have attracted considerable interest in this field, as they are capable of learning patterns in a collection of images and achieve clinically comparable levels of accuracy in steatosis grading. However, variations in pa...

Synthetic Lung Ultrasound Data Generation Using Autoencoder With Generative Adversarial Network.

Class imbalance is a significant challenge in medical image analysis, particularly in lung ultrasound (LUS), where severe patterns are often underrepr...

Microencapsulation of Lactobacillus cells utilizing the β-glucan-rich cell wall of Saccharomyces cerevisiae for enhanced stability and efficacy.

Probiotic encapsulation enhances bacterial stability and viability, yet conventional materials like sodium alginate have limitations, particularly in ...

A Deep Learning Approach for Mandibular Condyle Segmentation on Ultrasonography.

Deep learning techniques have demonstrated potential in various fields, including segmentation, and have recently been applied to medical image proces...

Keypoint localization and parameter measurement in ultrasound biomicroscopy anterior segment images based on deep learning.

BACKGROUND: Accurate measurement of anterior segment parameters is crucial for diagnosing and managing ophthalmic conditions, such as glaucoma, catara...

TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal ECG.

Noninvasive fetal ECG (FECG) monitoring holds significant importance in ensuring the normal development of the fetus. Since FECG is usually submerged ...

Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification.

Polycystic Ovarian Disease (PCOD) is among the most prevalent endocrine disorders complicating the health of innumerable women worldwide due to lack o...

Artificial intelligence guided imaging as a tool to fill gaps in health care delivery.

Deep vein thrombosis (DVT) causes significant morbidity/mortality and timely diagnosis often via ultrasound is critical. However, the shortage of trai...

Identification of Key Mitochondrial Autophagy-Related Genes in Fetal Growth Restriction.

OBJECTIVE: To identify key mitochondrial autophagy-related genes (MARGs) in fetal growth restriction (FGR)and evaluate their diagnostic potential thro...

Prediction of high-risk pregnancy based on machine learning algorithms.

This study explores the application of machine learning algorithms in predicting high-risk pregnancy among expectant mothers, aiming to construct an e...

Multimodal GPT model for assisting thyroid nodule diagnosis and management.

Although using artificial intelligence (AI) to analyze ultrasound images is a promising approach to assessing thyroid nodule risks, traditional AI mod...

Automatic ultrasound image alignment for diagnosis of pediatric distal forearm fractures.

PURPOSE: The study aims to develop an automatic method to align ultrasound images of the distal forearm for diagnosing pediatric fractures. This appro...

Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model.

BACKGROUND: This study was performed to characterize the relationship of various laboratory test indicators with clinical information and Preeclampsia...

Performance of radiomics analysis in ultrasound imaging for differentiating benign from malignant adnexal masses: A systematic review and meta-analysis.

INTRODUCTION: We present the state of the art of ultrasound-based machine learning (ML) radiomics models in the context of ovarian masses and analyze ...

Self-supervised learning for label-free segmentation in cardiac ultrasound.

Segmentation and measurement of cardiac chambers from ultrasound is critical, but laborious and poorly reproducible. Neural networks can assist, but s...

Development of an artificial intelligence powered software for automated analysis of skeletal muscle ultrasonography.

Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analy...

Construction of a Multimodal Machine Learning Model for Papillary Thyroid Carcinoma Based on Pathomics and Ultrasound Radiomics Dataset.

The use of machine learning to integrate and analyse multimodal information has broad prospects for enhancing the precision of tumour diagnosis. Our s...

[Artificial intelligence and ultrasound in fetal medicine].

Ultrasound is essential in fetal medicine for diagnosing and monitoring, but it requires extensive training. Artificial intelligence (AI) shows a grea...

A deep learning-based multimodal medical imaging model for breast cancer screening.

In existing breast cancer prediction research, most models rely solely on a single type of imaging data, which limits their performance. To overcome t...