AIMC Topic: Ultrasonography, Prenatal
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Artificial intelligence based automatic classification, annotation, and measurement of the fetal heart using HeartAssist.
Scientific reports
Apr 16, 2025
This study evaluated the feasibility of HeartAssist, a novel automated tool designed for classification of fetal cardiac views, annotation of cardiac structures, and measurement of cardiac parameters. Unlike previous AI tools that primarily focused o...
Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing.
BMC pregnancy and childbirth
Mar 31, 2025
BACKGROUND: Regular auditing of ultrasound images is required to maintain quality; however, manual auditing is time-consuming and can be inconsistent. We therefore aimed to develop and validate an artificial intelligence-based image quality audit (AI...
Automatic Human Embryo Volume Measurement in First Trimester Ultrasound From the Rotterdam Periconception Cohort: Quantitative and Qualitative Evaluation of Artificial Intelligence.
Journal of medical Internet research
Mar 31, 2025
BACKGROUND: Noninvasive volumetric measurements during the first trimester of pregnancy provide unique insight into human embryonic growth and development. However, current methods, such as semiautomatic (eg, virtual reality [VR]) or manual segmentat...
Cesarean Scar Pregnancy Prognostic Classification System Based on Machine-Learning and Traditional Linear Scoring Models.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Mar 26, 2025
OBJECTIVES: Cesarean scar pregnancy (CSP) refers to a special type of pregnancy with a variable prognosis. We aimed to establish a prognostic classification system using ultrasound and clinical features to provide a reference for management strategie...
The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review.
Medicina (Kaunas, Lithuania)
Mar 21, 2025
Artificial intelligence (AI) is rapidly gaining attention in radiology and cardiology for accurately diagnosing structural heart disease. In this review paper, we first outline the technical background of AI and echocardiography and then present an a...
Volume-based complete automation for ultrasound fetal biometry: A pilot approach to assess feasibility, reliability, and perspectives.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Mar 18, 2025
BACKGROUND: Detection algorithms targeting anatomic landmarks in three-dimensional (3D) ultrasound (US) volume (three-dimensional US) appear to be a relevant and easy-to-implement option to address junior and occasional operators' difficulties in pro...
Towards automatic US-MR fetal brain image registration with learning-based methods.
NeuroImage
Mar 7, 2025
Fetal brain imaging is essential for prenatal care, with ultrasound (US) and magnetic resonance imaging (MRI) providing complementary strengths. While MRI has superior soft tissue contrast, US offers portable and inexpensive screening of neurological...
Prediction of clinical risk factors in pregnancy using optimized neural network scheme.
Placenta
Mar 4, 2025
Women should be aware of prenancy related health issues. A user-friendly model is developed in which the patients can use as well as clinicians to determine the risks associated with foetal development inside the womb, birth weight, whose effects are...
AI and early diagnostics: mapping fetal facial expressions through development, evolution, and 4D ultrasound.
Journal of perinatal medicine
Feb 14, 2025
The development of facial musculature and expressions in the human fetus represents a critical intersection of developmental biology, neurology, and evolutionary anthropology, offering insights into early neurological and social development. Fetal fa...
Video Clip Extraction From Fetal Ultrasound Scans Using Artificial Intelligence to Allow Remote Second Expert Review for Congenital Heart Disease.
Prenatal diagnosis
Feb 6, 2025
OBJECTIVE: To use artificial intelligence (AI) to automatically extract video clips of the fetal heart from a stream of ultrasound video, and to assess the performance of these when used for remote second review.
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