AIMC Topic: Fetal Growth Retardation

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A Machine Learning-Based Intrauterine Growth Restriction (IUGR) Prediction Model for Newborns.

Indian journal of pediatrics
Intrauterine growth restriction (IUGR) is a condition in which the fetal weight is below the 10th percentile for its gestational age. Prenatal exposure to metals can cause a decrease in fetal growth during gestation thereby reducing birth weight. The...

Machine learning methods to predict attrition in a population-based cohort of very preterm infants.

Scientific reports
The timely identification of cohort participants at higher risk for attrition is important to earlier interventions and efficient use of research resources. Machine learning may have advantages over the conventional approaches to improve discriminati...

Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning Techniques.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Intrauterine Growth Restriction (IUGR) is a condition in which a fetus does not grow to the expected weight during pregnancy. There are several well documented causes in the literature for this issue, such as maternal disord...

Multiscale Road Extraction in Remote Sensing Images.

Computational intelligence and neuroscience
Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture b...

Convolutional Neural Networks for Recognition of Lymphoblast Cell Images.

Computational intelligence and neuroscience
This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia (ALL) subtypes. The two ALL subtypes considered are T-lymphoblastic leukaemia (pre-T) and B-lymphoblastic leukaemia (pre-B). They exhibit various characterist...

Decision Fusion-Based Fetal Ultrasound Image Plane Classification Using Convolutional Neural Networks.

Ultrasound in medicine & biology
Machine learning for ultrasound image analysis and interpretation can be helpful in automated image classification in large-scale retrospective analyses to objectively derive new indicators of abnormal fetal development that are embedded in ultrasoun...

Learning and combining image neighborhoods using random forests for neonatal brain disease classification.

Medical image analysis
It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensiona...

Fetal growth disorders detection during first trimester gestation through comprehensive maternal circulating DNA profiling.

Human molecular genetics
BACKGROUND: Early diagnosis, close follow-up and timely delivery constitute the main elements for appropriate detection and management of Fetal Growth Disorders (FGD). We hypothesized that fetoplacental FGD-associated alterations can be detected in c...

Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study.

Journal of medical Internet research
BACKGROUND: Fetal growth restriction (FGR) is a common complication of preeclampsia. FGR in patients with preeclampsia increases the risk of neonatal-perinatal mortality and morbidity. However, previous prediction methods for FGR are class-biased or ...