Pregnancy

Multi-view constrained semi-supervised vertebra detection for 3D ultrasound spine volume.

BACKGROUND: Scoliosis is a spinal disorder characterized by a three-dimensional (3D) deformity of the vertebral column. 3D ultrasound imaging has been used for scoliosis assessment, and automatic vertebra detection is important for objective evaluation of spinal deformity. However, existing automatic assessment methods are mainly based on two-dimensional (2D) projection images and require large am...

Time-series prediction of adverse birth outcomes in the U.S. using multilayer perceptron neural networks.

Adverse birth outcomes (ABOs), including preterm birth, low birth weight, low Apgar scores, and neonatal mortality, remain major public health challenges in the United States and disproportionately affect racial, socioeconomic, and demographic subgroups. A time-series multilayer perceptron (MLP) neural network was developed to predict ABOs and identify populations with elevated risk using U.S. Vit...

Letter to the editor: Machine learning model for predicting preeclampsia-related adverse outcomes.

Artificial Intelligence Interpretation of Point-of-Care Lung Ultrasound in Microgravity.

INTRODUCTION: Long-duration space missions demand reliable, portable, and autonomous medical diagnostic tools. Lung ultrasound (LUS) is ideal for spac...

Cardiac Natural Mechanical Wave Detection and Speed Estimation Using Deep Learning-Based 2-D Ultrasound Imaging: A Feasibility Study.

OBJECTIVE: Cardiac natural mechanical wave (NMW) propagation speed is a marker of the mechanical properties and pathological state of tissue, and its ...

An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis.

Characterizing in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building on prior spatiotemporal fetal...

The fetoneonatology fellowship: a unified subspecialty for the fetal-neonatal continuum.

Advances in fetal diagnosis and therapy have created a need for clinicians with expertise spanning prenatal assessment and neonatal care. Current mate...

A novel cross-attentive network for classifying cervical metastatic lymph nodes on B- and D-mode ultrasound images in oral squamous cell carcinoma.

OBJECTIVES: This study proposes a deep convolutional neural network model that integrates B-mode and D-mode ultrasound images to classify metastatic l...

Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet).

Motor imitation impairments are commonly reported in individuals with autism spectrum conditions (ASCs), suggesting that motor imitation could be used...

From Speech to Sonography: Spectral Networks for Ultrasound Microstructure Classification.

The frequency dependence of backscattered radiofrequency (RF) signals produced by ultrasound scanners carries rich information related to the tissue m...

Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures.

BACKGROUND: Patient-specific quality assurance (PSQA) plays a pivotal role in intensity-modulated radiotherapy (IMRT) to ensure accurate dose delivery...

Identification of preeclampsia-associated immune-related risk loci in the Chinese population.

OBJECTIVE: Preeclampsia, the most severe hypertensive disorder of pregnancy, is the leading cause of maternal death. While genetic factors are recogni...

Multi-Center Adversarial Bi-Phase Cross-Attention Network for Right Ventricular Segmentation and Functional Classification in Echocardiography.

BACKGROUND: Automated right ventricular (RV) analysis in 2D echocardiography is limited by the morphological complexity of RV segmentation and the dom...

Ultrasound-Based AI in Predicting Hormone Receptor Status in Breast Cancer: Is "Digital Biopsy" Possible.

Accurate and rapid determination of tumor histopathological features and molecular subtypes is critical for breast cancer prognosis and treatment stra...

Predicting abnormal birth weight and identifying associated factors using machine learning in the Hararghe Health and Demographic Surveillance System, Ethiopia.

BACKGROUND: Birth weight is a reliable indicator of intrauterine growth and an important predictor of neonatal survival, growth, and long-term develop...

The evolution of ultrasound in sarcopenia assessment: Global trends, technological shifts, and clinical frontiers.

BackgroundSarcopenia is a progressive skeletal muscle disorder associated with increased disability, morbidity, and mortality. Ultrasound has gained i...

Machine learning-based prediction model for adverse pregnancy outcomes in women with gestational diabetes mellitus.

AIMS/INTRODUCTION: Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications. Investigating clinical risk factors for ad...

Prenatal exposure to particulate matter and risk of autism spectrum disorder in children: a population-based cohort study in South Korea.

BACKGROUND: Prenatal exposure to particulate matter has been investigated as a potential environmental factor for autism spectrum disorder (ASD), but ...

Lung ultrasound in interstitial lung disease: from bedside screening to prognostic integration and AI-assisted standardization.

Interstitial lung diseases (ILDs) require early recognition and longitudinal assessment, yet repeated high-resolution computed tomography (HRCT) is of...

Region-Specific Evaluation of Plaque Segmentation in Cross-sectional Projections of Carotid Ultrasound Images Using Deep Learning Models in a Sub-clinical Atherosclerosis Cohort.

OBJECTIVE: Detection of atherosclerotic plaque in the carotid arteries is essential for early cardiovascular risk assessment. While B-mode ultrasound ...