AIMC Topic: Ultrasonography, Prenatal
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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...
Longitudinal twin growth discordance patterns and adverse perinatal outcomes.
American journal of obstetrics and gynecology
Jan 7, 2025
BACKGROUND: Growth discordance in twin pregnancies is associated with increased perinatal morbidity and mortality, yet the patterns of discordance progression and the utility of Doppler assessments remain underinvestigated.
CLP-Net: an advanced artificial intelligence technique for localizing standard planes of cleft lip and palate by three-dimensional ultrasound in the first trimester.
BMC pregnancy and childbirth
Jan 7, 2025
BACKGROUND: Early diagnosis of cleft lip and palate (CLP) requires a multiplane examination, demanding high technical proficiency from radiologists. Therefore, this study aims to develop and validate the first artificial intelligence (AI)-based model...
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...
Latent representation learning for classification of the Doppler ultrasound images.
Computers in biology and medicine
Dec 26, 2024
The classification of Doppler ultrasound images plays an important role in the diagnosis of pregnancy. However, it is a challenging problem that suffers from a variable length of these images with a dimension gap between them. In this study, we propo...
Predicting preterm birth using electronic medical records from multiple prenatal visits.
BMC pregnancy and childbirth
Dec 21, 2024
This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation ...
Adult
Sensitivity and Specificity
Ultrasonography, Prenatal
Logistic Models
Predictive Value of Tests
Prenatal Care
Risk Assessment
Female
Infant, Newborn
Premature Birth
Young Adult
Humans
Area Under Curve
Machine Learning
Gestational Age
Electronic Health Records
Cervical Length Measurement
Pregnancy
Cost-effectiveness analysis of AI-based image quality control for perinatal ultrasound screening.
BMC medical education
Dec 18, 2024
PURPOSE: This study aimed to compare the cost-effectiveness of AI-based approaches with manual approaches in ultrasound image quality control (QC).
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 ...
Application of artificial intelligence in VSD prenatal diagnosis from fetal heart ultrasound images.
BMC pregnancy and childbirth
Nov 16, 2024
BACKGROUND: Developing a combined artificial intelligence (AI) and ultrasound imaging to provide an accurate, objective, and efficient adjunctive diagnostic approach for fetal heart ventricular septal defects (VSD).
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