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Ultrasonography, Prenatal

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Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization.

Scientific reports
The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of hypertensive pregnancy disorders. Moreover, a timely diagnosis of placenta previa may save lives. Ultrasound is the primary imaging modality in pregnan...

Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart.

Computational and mathematical methods in medicine
Congenital heart defect (CHD) refers to the overall structural abnormality of the heart or large blood vessels in the chest cavity. It is the most common type of fetal congenital defects. Prenatal diagnosis of congenital heart disease can improve the...

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.

DeepGA for automatically estimating fetal gestational age through ultrasound imaging.

Artificial intelligence in medicine
Accurate estimation of gestational age (GA) is vital for identifying fetal abnormalities. Conventionally, GA is estimated by measuring the morphology of the cranium, abdomen, and femur manually and inputting them into the classic Hadlock formula to a...

Gaze-assisted automatic captioning of fetal ultrasound videos using three-way multi-modal deep neural networks.

Medical image analysis
In this work, we present a novel gaze-assisted natural language processing (NLP)-based video captioning model to describe routine second-trimester fetal ultrasound scan videos in a vocabulary of spoken sonography. The primary novelty of our multi-mod...

Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester.

PloS one
OBJECTIVE: To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester.

An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images.

BioMed research international
Cerebellum measures taken from routinely obtained ultrasound (US) images have been frequently employed to determine gestational age and identify developing central nervous system's anatomical abnormalities. Standardized cerebellar assessments from la...

Deep Learning Algorithm-Based Ultrasound Image Information in Diagnosis and Treatment of Pernicious Placenta Previa.

Computational and mathematical methods in medicine
This study was to explore the value of the deep dictionary learning algorithm in constructing a B ultrasound scoring system and exploring its application in the clinical diagnosis and treatment of pernicious placenta previa (PPP). 60 patients with PP...

Task model-specific operator skill assessment in routine fetal ultrasound scanning.

International journal of computer assisted radiology and surgery
PURPOSE: For highly operator-dependent ultrasound scanning, skill assessment approaches evaluate operator competence given available data, such as acquired images and tracked probe movement. Operator skill level can be quantified by the completeness,...

Deep learning-based plane pose regression in obstetric ultrasound.

International journal of computer assisted radiology and surgery
PURPOSE: In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a major challenge in skill acquisition. We aim to build a US plane local...