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Fetus

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Artificial intelligence applied to fetal MRI: A scoping review of current research.

The British journal of radiology
Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to '...

Imaging fetal anatomy.

Seminars in cell & developmental biology
Due to advancements in ultrasound techniques, the focus of antenatal ultrasound screening is moving towards the first trimester of pregnancy. The early first trimester however remains in part, a 'black box', due to the size of the developing embryo a...

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography.

Sensors (Basel, Switzerland)
Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-qualit...

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.

Sensors (Basel, Switzerland)
Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient's outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard...

How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10-14 in ultrasound using deep learning.

Computer methods and programs in biomedicine
PURPOSE: To investigate if artificial intelligence can identify fetus intracranial structures in pregnancy week 11-14; to provide an automated method of standard and non-standard sagittal view classification in obstetric ultrasound examination METHOD...

Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta.

Pediatric and developmental pathology : the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society
Digital imaging, including the use of artificial intelligence, has been increasingly applied to investigate the placenta and its related pathology. However, there has been no comprehensive review of this body of work to date. The aim of this study wa...

Artificial intelligence to understand fluctuation of fetal brain activity by recognizing facial expressions.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: To examine whether artificial intelligence can achieve discoveries regarding fetal brain activity.

Automatic Segmentation of the Fetus in 3D Magnetic Resonance Images Using Deep Learning: Accurate and Fast Fetal Volume Quantification for Clinical Use.

Pediatric cardiology
Magnetic resonance imaging (MRI) provides images for estimating fetal volume and weight, but manual delineations are time consuming. The aims were to (1) validate an algorithm to automatically quantify fetal volume by MRI; (2) compare fetal weight by...

Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep-learning-based analysis.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVE: Despite decades of obstetric scanning, the field of sonographer workflow remains largely unexplored. In the second trimester, sonographers use scan guidelines to guide their acquisition of standard planes and structures; however, the scan-...

Deep Learning-Based Multiclass Brain Tissue Segmentation in Fetal MRIs.

Sensors (Basel, Switzerland)
Fetal brain tissue segmentation is essential for quantifying the presence of congenital disorders in the developing fetus. Manual segmentation of fetal brain tissue is cumbersome and time-consuming, so using an automatic segmentation method can great...