AIMC Topic: Gestational Age

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Fetal growth and gestational age prediction by machine learning.

The Lancet. Digital health Jun 23, 2020
Humans Machine Learning Prospective Studies Cohort Studies Gestational Age Fetal Development
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Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study.

The Lancet. Digital health Jun 23, 2020
BACKGROUND: Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population's general health and nutritional status. Current clinical methods of estimating fetal gestati...
Data Accuracy Humans Internationality Machine Learning Gestational Age Fetal Development Algorithms Prospective Studies Biometry Pregnancy Female Ultrasonography
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A preliminary study to quantitatively evaluate the development of maturation degree for fetal lung based on transfer learning deep model from ultrasound images.

International journal of computer assisted radiology and surgery Jun 15, 2020
PURPOSE: The evaluation of fetal lung maturity is critical for clinical practice since the lung immaturity is an important cause of neonatal morbidity and mortality. For the evaluation of the development of fetal lung maturation degree, our study est...
Female Infant, Newborn Ultrasonography, Prenatal Lung Gestational Age Humans Machine Learning Pregnancy
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Variability in Plus Disease Identified Using a Deep Learning-Based Retinopathy of Prematurity Severity Scale.

Ophthalmology. Retina May 4, 2020
PURPOSE: Retinopathy of prematurity is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective, which leads to treatment differences. Our goal was to determine objective differences in the diagnosis of plus disease betw...
Humans Algorithms Severity of Illness Index Deep Learning Retrospective Studies Gestational Age Female Retinopathy of Prematurity Infant, Newborn Male
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Prediction of vaginal birth after cesarean deliveries using machine learning.

American journal of obstetrics and gynecology Jan 30, 2020
BACKGROUND: Efforts to reduce cesarean delivery rates to 12-15% have been undertaken worldwide. Special focus has been directed towards parturients who undergo a trial of labor after cesarean delivery to reduce the burden of repeated cesarean deliver...
Tertiary Care Centers Feasibility Studies Head Gestational Age Risk Assessment ROC Curve Organ Size Area Under Curve Trial of Labor Uterine Rupture Vaginal Birth after Cesarean Delivery, Obstetric Apgar Score Fetal Weight Extraction, Obstetrical Retrospective Studies Pregnancy Female Infant, Newborn Cesarean Section Parity Risk Factors Adult Humans Machine Learning
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Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning.

Artificial intelligence in medicine Nov 17, 2019
Obstetric ultrasound examination of physiological parameters has been mainly used to estimate the fetal weight during pregnancy and baby weight before labour to monitor fetal growth and reduce prenatal morbidity and mortality. However, the problem is...
Fetal Development Models, Statistical Fetal Weight Humans Predictive Value of Tests Female Birth Weight Algorithms Machine Learning Adult Gestational Age Pregnancy Ultrasonography
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Supervised classification approach of biometric measures for automatic fetal defect screening in head ultrasound images.

Journal of medical engineering & technology Sep 10, 2019
This paper presents an advanced approach for foetal brain abnormalities diagnostic by integrating significant biometric features in the identification process. In foetal anomaly diagnosis, manual evaluation of foetal behaviour in ultrasound images is...
Humans Biometry Prenatal Diagnosis Support Vector Machine Databases, Factual Brain Head Ultrasonography Fetus Image Processing, Computer-Assisted Gestational Age
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Prediction model development of late-onset preeclampsia using machine learning-based methods.

PloS one Aug 23, 2019
Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its prediction is essential to its prompt management. This study aimed to develop models using machine learning...
Adult Pre-Eclampsia Pregnancy Trimester, Third Humans Logistic Models Support Vector Machine Age of Onset Machine Learning Bayes Theorem Pregnancy Trimester, Second Pregnancy Gestational Age Female
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Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

Artificial intelligence in medicine Jul 25, 2019
OBJECTIVE: The neonatal period of a child is considered the most crucial phase of its physical development and future health. As per the World Health Organization, India has the highest number of pre-term births [1], with over 3.5 million babies born...
Heart Rate Datasets as Topic Infant, Premature Gestational Age Intensive Care Units, Neonatal Clinical Decision Rules Apnea India Support Vector Machine Decision Trees Birth Weight Humans Algorithms Neural Networks, Computer Infant, Newborn Infant, Extremely Premature Deep Learning
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Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants.

Journal of Korean medical science Apr 29, 2019
BACKGROUND: Little research based on the artificial neural network (ANN) is done on preterm birth (spontaneous preterm labor and birth) and its major determinants. This study uses an ANN for analyzing preterm birth and its major determinants.
Adult ROC Curve Logistic Models Hypertension Placenta Diabetes Mellitus Humans Female Body Mass Index Gestational Age Obstetric Labor, Premature Neural Networks, Computer Infant, Newborn Cervix Uteri Pregnancy Premature Birth Area Under Curve Risk Factors
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