AIMC Topic: Fetus
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An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images.
Scientific reports
Jul 1, 2025
Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormalities related to various diseases. The manual classification of these is a hectic and time-consuming pr...
Advancing prenatal healthcare by explainable AI enhanced fetal ultrasound image segmentation using U-Net++ with attention mechanisms.
Scientific reports
Jun 4, 2025
Prenatal healthcare development requires accurate automated techniques for fetal ultrasound image segmentation. This approach allows standardized evaluation of fetal development by minimizing time-exhaustive processes that perform poorly due to human...
TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal ECG.
IEEE journal of biomedical and health informatics
May 6, 2025
Noninvasive fetal ECG (FECG) monitoring holds significant importance in ensuring the normal development of the fetus. Since FECG is usually submerged by maternal ECG (MECG) and background noise in abdominal ECG (AECG), it is challenging to exactly re...
FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI.
NeuroImage
Apr 10, 2025
Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. Longitudinal and cross-sectional studies of fetal dMRI have the potential to reveal subtle but crucial changes associated with normal and abnormal neu...
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...
Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning.
Sensors (Basel, Switzerland)
Jan 22, 2025
Accurate measurement of frontomaxillary facial (FMF) angles in prenatal ultrasound (US) scans plays a pivotal role in the screening of trisomy 21. Nevertheless, this intricate procedure heavily relies on the proficiency of the ultrasonographer and te...
Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation.
Scientific reports
Jan 15, 2025
We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-l...
TKR-FSOD: Fetal Anatomical Structure Few-Shot Detection Utilizing Topological Knowledge Reasoning.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Fetal multi-anatomical structure detection in ultrasound (US) images can clearly present the relationship and influence between anatomical structures, providing more comprehensive information about fetal organ structures and assisting sonographers in...
Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection.
BMC medical imaging
Dec 18, 2024
Ultrasound (US) imaging is an essential diagnostic technique in prenatal care, enabling enhanced surveillance of fetal growth and development. Fetal ultrasonography standard planes are crucial for evaluating fetal development parameters and detecting...
LPC-SonoNet: A Lightweight Network Based on SonoNet and Light Pyramid Convolution for Fetal Ultrasound Standard Plane Detection.
Sensors (Basel, Switzerland)
Nov 25, 2024
The detection of fetal ultrasound standard planes (FUSPs) is important for the diagnosis of fetal malformation and the prevention of perinatal death. As a promising deep-learning technique in FUSP detection, SonoNet's network parameters have a large ...
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