AIMC Topic: Gestational Age
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Predicting early mortality and severe intraventricular hemorrhage in very-low birth weight preterm infants: a nationwide, multicenter study using machine learning.
Scientific reports
May 12, 2024
Our aim was to develop a machine learning-based predictor for early mortality and severe intraventricular hemorrhage (IVH) in very-low birth weight (VLBW) preterm infants in Taiwan. We collected retrospective data from VLBW infants, dividing them int...
Machine Learning to Improve Accuracy of Transcutaneous Bilirubinometry.
Neonatology
Apr 29, 2024
INTRODUCTION: This study aimed to develop models for predicting total serum bilirubin by correcting errors of transcutaneous bilirubin using machine learning based on neonatal biomarkers that could affect spectrophotometric measurements of tissue bil...
Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study.
The Journal of pediatrics
Mar 30, 2024
OBJECTIVE: The objective of this study was to predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximeter and ventilator data.
Machine learning-based analysis for prediction of surgical necrotizing enterocolitis in very low birth weight infants using perinatal factors: a nationwide cohort study.
European journal of pediatrics
Mar 30, 2024
Early prediction of surgical necrotizing enterocolitis (sNEC) in preterm infants is important. However, owing to the complexity of the disease, identifying infants with NEC at a high risk for surgical intervention is difficult. We developed a machine...
The role of cell-free DNA biomarkers and patient data in the early prediction of preeclampsia: an artificial intelligence model.
American journal of obstetrics and gynecology
Mar 2, 2024
BACKGROUND: Accurate individualized assessment of preeclampsia risk enables the identification of patients most likely to benefit from initiation of low-dose aspirin at 12 to 16 weeks of gestation when there is evidence for its effectiveness, and ena...
Artificial intelligence assistance for fetal development: evaluation of an automated software for biometry measurements in the mid-trimester.
BMC pregnancy and childbirth
Feb 23, 2024
BACKGROUND: This study presents CUPID, an advanced automated measurement software based on Artificial Intelligence (AI), designed to evaluate nine fetal biometric parameters in the mid-trimester. Our primary objective was to assess and compare the CU...
Deep learning to estimate gestational age from fly-to cineloop videos: A novel approach to ultrasound quality control.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Jan 8, 2024
OBJECTIVE: Low-cost devices have made obstetric sonography possible in settings where it was previously unfeasible, but ensuring quality and consistency at scale remains a challenge. In the present study, we sought to create a tool to reduce substand...
A Machine Learning Algorithm using Clinical and Demographic Data for All-Cause Preterm Birth Prediction.
American journal of perinatology
Dec 4, 2023
OBJECTIVE: Preterm birth remains the predominant cause of perinatal mortality throughout the United States and the world, with well-documented racial and socioeconomic disparities. To develop and validate a predictive algorithm for all-cause preterm ...
Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study.
BJOG : an international journal of obstetrics and gynaecology
Nov 20, 2023
OBJECTIVES: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these m...
Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.
European radiology
Nov 14, 2023
OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphom...
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