Pregnancy

Speckle Noise Reduction Techniques in Ultrasound Imaging: A comprehensive review of the last two decades (2005-2024).

Ultrasound imaging has become a widely used medical modality over the past few decades. Despite technological advances, ultrasound images are susceptible to inherent noise that arises from tissue inhomogeneities and other acquisition-related uncertainties. The presence of noise degrades image quality and impacts diagnostic accuracy, necessitating the development of effective denoising techniques. ...

Large language models accurately extract aortic information from abdominal imaging reports in a large, real-world database.

OBJECTIVE: Maintaining robust surveillance programs for abdominal aortic aneurysms (AAAs) is important, but these programs are expensive and labor-intensive, typically requiring manual data review by trained health care professionals. Studies have shown that natural language processing software can assist in these functions, but each task-specific algorithm requires human-directed training before ...

BreAST-U²Net: A Twin-Stream U2Net with Attention-based Tumor Fusion for 2-D Tumor Segmentation in Automated Breast Ultrasound.

BACKGROUND: Automated breast ultrasound (ABUS) shows potential for breast cancer diagnosis but faces tumor segmentation challenges due to limited anno...

Unraveling Shared Diagnostic Biomarkers: Integrated Bioinformatics Identification of CGB7, MYCNUT, and GPR32 in Gestational Diabetes Mellitus and Retinopathy of Prematurity.

The relationship between Gestational Diabetes Mellitus (GDM) and Retinopathy of Prematurity (ROP) is not fully understood, but both conditions may sha...

Fetal cardiac remodeling in second trimester in pregnancies with pre-eclampsia and/or fetal growth restriction: deep-learning-based approach using population-wide data.

OBJECTIVE: Pre-eclampsia (PE) and fetal growth restriction (FGR) have been shown to impact fetal cardiac remodeling in the third trimester and postnat...

A Hybrid Model Combining U-Net and Transformers for Joint Segmentation and Beamforming of Plane-wave Ultrasound Images.

OBJECTIVE: Recent advancements in deep learning have shown significant potential in ultrasound imaging. However, most approaches focus solely on image...

Ultrasound super-resolved hemodynamic estimation in microvessel using physics-informed neural networks and data assimilation.

BACKGROUND AND OBJECTIVE: Ultrasound super-resolution imaging (SRI) enables the visualization of microvascular structure and velocity, but enhancing t...

Artificial intelligence methods in gestational diabetes mellitus prediction: A systematic literature review.

Gestational diabetes mellitus (GDM) is the most common metabolic disorder in pregnancy, posing risks to both maternal and neonatal health. Artificial ...

Golay-Net: Deep learning-based Golay coded excitation for ultrasound imaging.

Ultrasound imaging modality, which operates by transmitting and receiving short ultrasound pulses, offers a promising approach for real-time, high-res...

Machine learning-based prediction of postpartum hemorrhage using maternal clinical and biochemical features.

OBJECTIVE: This study evaluates the predictive performance of various machine learning (ML) algorithms for postpartum hemorrhage (PPH), peripartum hys...

Attention-Based Multimodal Deep Learning for Uveal Melanoma Classification Using Ultra-Widefield Fundus Images and Ocular Ultrasound.

PURPOSE: To develop and evaluate a deep learning model that integrates ultra-widefield fundus photography and B-scan ultrasonography for automated cla...

Automatic Follicle Counting From Ultrasound Images of Ovaries Using MARDSE-UNET Model.

Detecting ovarian structures in ultrasound images is essential in gynecological and reproductive medicine. An automated detection system can serve as ...

Artificial intelligence-driven precision treatment of reproductive medicine-related diseases: the optimal protocol choice for IVF-ET.

INTRODUCTION: Optimal ovarian stimulation (OS) selection is critical for IVF success, but expert-based decisions often lack consistency in outcomes, c...

Investigating the potential of combining Raman spectroscopy and machine learning for gestational age classification in rat cervical tissue.

Spontaneous preterm birth (SPB) is a leading cause of neonatal morbidity and mortality worldwide. It occurs when the uterine cervix (UC) opens prematu...

A Simplified and Data-Driven Lung Ultrasound Approach for Predicting Surfactant Need in Preterm Infants: A Pilot Study Using Machine Learning and Rule-Based Models.

OBJECTIVES: Six-region lung ultrasound (LUS) scores show good predictive value for predicting surfactant need in preterm infants but rely on a fixed t...

High-precision wavefield simulation and deep learning-based sound speed reconstruction for transcranial ultrasound imaging.

Transcranial ultrasound imaging plays an important role in the diagnosis of brain diseases and the monitoring of brain function. However, the quality ...

Identification of GPX3 as a key biomarker of placental ferroptosis in gestational diabetes mellitus via bioinformatics and clinical analysis.

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance during pregnancy, and emerging evidence implicates dysregulated iron metab...

The effect of autonomous mobile robots on improving endoscope reprocessing efficiency: a prospective comparative trial (with video).

BACKGROUND AND AIMS: Autonomous mobile robots (AMRs) have an increasingly wide range of medical applications. However, their use in endoscopy centers ...

Deep learning with multimodal Raman spectral fusion framework: An analytical approach for microalgal lipid quantification.

As a key feedstock for sustainable bioenergy, microalgae require precise regulation of lipid synthesis and accurate detection methods. To efficiently ...

Machine learning demonstrates normal fetal Doppler velocimetry associated with reduced risk of necrotizing enterocolitis among preterm infants with growth restriction.

BackgroundNecrotizing enterocolitis (NEC) is an intestinal ischemic disease that affects preterm infants with fetal growth restriction (FGR). The role...