Pregnancy

Integrated Ultrasound Device for Precision Bladder Volume Monitoring via Acoustic Focusing and Machine Learning.

Bladder volume monitoring is critical for managing lower urinary tract dysfunctions, yet existing methods remain invasive or operator-dependent and are unsuitable for continuous use. Here, we present a conformable wearable ultrasound system that combines lens-assisted acoustic focusing with machine-learning regression to enable non-invasive bladder volume estimation, while providing a clear path t...

First trimester prediction of gestational diabetes mellitus by machine learning in twin pregnancies.

INTRODUCTION: We aimed to develop a machine learning model for first-trimester prediction of gestational diabetes mellitus (GDM) in twin pregnancies using a prospective international, multi-center cohort and identify useful predictive markers. METHODS: Pregnant women with two live fetuses were enrolled at 11 + 0 to 13 + 6 weeks' gestation and followed until delivery. GDM was diagnosed at 24-28 wee...

Digital maternity care in Germany: a cross-sectional web-based survey on midwives' perceptions.

PURPOSE: Maternity care is a central component of any healthcare system and is largely provided by midwives. Considering increasing cost pressures and...

FetCAT: Cross-attention fusion of transformer-CNN architecture for fetal brain plane classification with explainability using motion-degraded MRI.

Fetal brain magnetic resonance imaging (MRI) has been recognized as a vital diagnostic tool for identifying neurological anomalies during pregnancy. A...

Time-Series Machine Learning for Prediction of Bronchopulmonary Dysplasia.

OBJECTIVE: To build a time-series machine learning (ML) model that improves bronchopulmonary dysplasia (BPD) prediction compared with published online...

Machine Learning Prediction Models for Preeclampsia: Systematic Review and Meta-Analysis.

BACKGROUND: Preeclampsia is a severe hypertensive disorder with rising global prevalence. While machine learning (ML) models for predicting preeclamps...

Recognition of Normal Fetal Echocardiograms Based on an Explainable Denoising Deep Learning Model.

PURPOSE: To evaluate the proposed explainable denoising deep learning model, Grouped Shared Convolutional Attention Vision Transformer (GSCAViT), for ...

Gene Modification: Exploring the potential in treating kidney diseases.

Chronic kidney disease (CKD) is a leading cause of death worldwide. Currently available drugs slow but do not cure or prevent progression to end-stage...

A novel framework for fully automated co-registration of intravascular ultrasound and optical coherence tomography imaging data.

AIMS: To develop a deep-learning (DL) framework that enables fully automated longitudinal and circumferential co-registration of intravascular ultraso...

Exploring Primary and Interaction Effects of Minor Physical Anomalies: Development and Validation of Prediction Models Using Explainable Machine Learning Algorithms for Early-Onset Schizophrenia.

BACKGROUND AND HYPOTHESIS: Minor physical abnormalities (MPAs) are neurodevelopmental markers that can be traced to prenatal events and may be signifi...

Development and external validation of an interpretable machine learning-based model for obesity risk prediction in 2-18-year-old children and adolescents in Beijing and Tangshan.

BACKGROUND: The multifactorial mechanisms driving childhood obesity, a global public health challenge, are yet to be fully elucidated. We aimed to dev...

Identification and validation of PANX1 as an inflammasome-related biomarker in gestational diabetes mellitus: insights from machine learning and experimental approaches.

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance during pregnancy, resulting from insulin resistance, and is associated wit...

NLP-ROPCare: predicting retinopathy of prematurity with admission notes using natural language processing.

OBJECTIVES: Retinopathy of prematurity (ROP) is a leading cause of blindness in children worldwide, requiring more efficient models to help predict tr...

Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg.

Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an important ...

Identification of cell senescence-related genes in spontaneous preterm birth based on bioinformatics analysis and machine learning.

Spontaneous premature birth (SPTB) is a common pregnancy complication; however, few studies have explored cell senescence-related markers in SPTB. Bio...

BraTioUS: A multicenter dataset of baseline intraoperative brain tumor ultrasound images.

The BraTioUS (Brain Tumor Intraoperative Ultrasound) dataset [1] is a large-scale, multicenter, and publicly available collection of intraoperative ul...

Machine learning-enhanced nanofiber systems: A new frontier in controlled drug release.

The use of machine learning (ML) is reshaping the design and optimization of nanofiber-based drug delivery systems (N-DDS). Electrospun nanofibers off...

Pediatric diabetes prediction using machine learning.

Diabetes is a chronic condition that affects a substantial portion of the global population and is linked to elevated mortality rates and a range of s...

Active guidance in ultrasound bladder scanning using reinforcement learning.

Accurate measurement of bladder volume is essential for diagnosing urinary retention and voiding dysfunction. However, finding optimal view can be cha...

Tuning pH for the Controlled Formation of Ultrasmall Nanoparticles with Direct Cytosolic Access.

Direct cytosolic delivery of nanoparticles offers advantages by circumventing the endocytic pathway, thereby reducing associated intracellular traffic...