AIMC Topic: Ultrasonography, Prenatal
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Development and validation of an artificial intelligence assisted prenatal ultrasonography screening system for trainees.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Oct 4, 2023
OBJECTIVE: Fetal anomaly screening via ultrasonography, which involves capturing and interpreting standard views, is highly challenging for inexperienced operators. We aimed to develop and validate a prenatal-screening artificial intelligence system ...
Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme?
Prenatal diagnosis
Sep 30, 2023
BACKGROUND: Artificial intelligence (AI) has the potential to improve prenatal detection of congenital heart disease. We analysed the performance of the current national screening programme in detecting hypoplastic left heart syndrome (HLHS) to compa...
Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility.
European radiology
Sep 2, 2023
OBJECTIVES: To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity ...
Automatic Deep Learning-Based Pipeline for Automatic Delineation and Measurement of Fetal Brain Structures in Routine Mid-Trimester Ultrasound Images.
Fetal diagnosis and therapy
Aug 11, 2023
INTRODUCTION: The aim of this study was to develop a pipeline using state-of-the-art deep learning methods to automatically delineate and measure several of the most important brain structures in fetal brain ultrasound (US) images.
Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease.
Acta obstetricia et gynecologica Scandinavica
Aug 10, 2023
INTRODUCTION: This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence.
Use of artificial intelligence and deep learning in fetal ultrasound imaging.
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
Jul 10, 2023
Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep-learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard im...
Combining deep learning and intelligent biometry to extract ultrasound standard planes and assess early gestational weeks.
European radiology
Jul 1, 2023
OBJECTIVES: To develop and validate a fully automated AI system to extract standard planes, assess early gestational weeks, and compare the performance of the developed system to sonographers.
Simulator, machine learning, and artificial intelligence: Time has come to assist prenatal ultrasound diagnosis.
Journal of clinical ultrasound : JCU
Jun 24, 2023
In this Commentary authors investigated and extended the role of simulator in assisting obstetric sonographers in training program. The interconnection of different digitalized technologies such as digital data, artificial neuronal and convolutional ...
Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning.
Journal of perinatal medicine
May 11, 2023
OBJECTIVES: Congenital heart defects (CHDs) are the most common birth defects. Recently, artificial intelligence (AI) was used to assist in CHD diagnosis. No comparison has been made among the various types of algorithms that can assist in the prenat...
Fetal brain activity and the free energy principle.
Journal of perinatal medicine
Apr 26, 2023
OBJECTIVES: To study whether the free energy principle can explain fetal brain activity and the existence of fetal consciousness via a chaotic dimension derived using artificial intelligence.
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