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Fetus

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Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation.

Sensors (Basel, Switzerland)
Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more...

Deep learning-based parameter estimation in fetal diffusion-weighted MRI.

NeuroImage
Diffusion-weighted magnetic resonance imaging (DW-MRI) of fetal brain is challenged by frequent fetal motion and signal to noise ratio that is much lower than non-fetal imaging. As a result, accurate and robust parameter estimation in fetal DW-MRI re...

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning.

Scientific reports
In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical d...

Attention-guided deep learning for gestational age prediction using fetal brain MRI.

Scientific reports
Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly c...

Deep robust residual network for super-resolution of 2D fetal brain MRI.

Scientific reports
Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial resolution by reconstructing high-resolution (HR) images from low-resolution (LR...

Novel artificial intelligence approach for automatic differentiation of fetal occiput anterior and non-occiput anterior positions during labor.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVES: To describe a newly developed machine-learning (ML) algorithm for the automatic recognition of fetal head position using transperineal ultrasound (TPU) during the second stage of labor and to describe its performance in differentiating be...

Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: MR imaging provides critical information about fetal brain growth and development. Currently, morphologic analysis primarily relies on manual segmentation, which is time-intensive and has limited repeatability. This work aimed...

Deep learning fetal ultrasound video model match human observers in biometric measurements.

Physics in medicine and biology
This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestati...

Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVES: To develop and validate an artificial intelligence system, the Prenatal ultrasound diagnosis Artificial Intelligence Conduct System (PAICS), to detect different patterns of fetal intracranial abnormality in standard sonographic reference ...