AIMC Topic: Gestational Age
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Risk factors for the time to development of retinopathy of prematurity in premature infants in Iran: a machine learning approach.
BMC ophthalmology
Aug 23, 2024
BACKGROUND: Retinopathy of prematurity (ROP), is a preventable leading cause of blindness in infants and is a condition in which the immature retina experiences abnormal blood vessel growth. The development of ROP is multifactorial; nevertheless, the...
Combining artificial intelligence and conventional statistics to predict bronchopulmonary dysplasia in very preterm infants using routinely collected clinical variables.
Pediatric pulmonology
Aug 16, 2024
BACKGROUND: Prematurity is the strongest predictor of bronchopulmonary dysplasia (BPD). Most previous studies investigated additional risk factors by conventional statistics, while the few studies applying artificial intelligence, and specifically ma...
Prediction models for retinopathy of prematurity occurrence based on artificial neural network.
BMC ophthalmology
Aug 5, 2024
INTRODUCTION: Early prediction and timely treatment are essential for minimizing the risk of visual loss or blindness of retinopathy of prematurity, emphasizing the importance of ROP screening in clinical routine.
Host-derived protein profiles of human neonatal meconium across gestational ages.
Nature communications
Jul 17, 2024
Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted ...
A novel artificial intelligence model for measuring fetal intracranial markers during the first trimester based on two-dimensional ultrasound image.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Jun 30, 2024
OBJECTIVE: To establish reference ranges of fetal intracranial markers during the first trimester and develop the first novel artificial intelligence (AI) model to measure key markers automatically.
Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.
Eye (London, England)
Jun 25, 2024
PURPOSE: To provide automatic detection of Type 1 retinopathy of prematurity (ROP), Type 2 ROP, and A-ROP by deep learning-based analysis of fundus images obtained by clinical examination using convolutional neural networks.
Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective.
International journal of medical informatics
Jun 19, 2024
BACKGROUND: Recent studies reveal that around 1.9 million stillbirths occur annually worldwide, with Sub-Saharan Africa having among the highest cases. Some Sub-Saharan African countries, including Ghana, failed to meet Millennium Development Goal 5 ...
Use of an Artificial Intelligence-Generated Vascular Severity Score Improved Plus Disease Diagnosis in Retinopathy of Prematurity.
Ophthalmology
Jun 10, 2024
PURPOSE: To evaluate whether providing clinicians with an artificial intelligence (AI)-based vascular severity score (VSS) improves consistency in the diagnosis of plus disease in retinopathy of prematurity (ROP).
Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta.
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
Jun 3, 2024
OBJECTIVE: Although remarkable strides have been made in fetal medicine and the prenatal diagnosis of congenital heart disease, around 60% of newborns with isolated coarctation of the aorta (CoA) are not identified prior to birth. The prenatal detect...
An innovative supervised longitudinal learning procedure of recurrent neural networks with temporal data augmentation: Insights from predicting fetal macrosomia and large-for-gestational age.
Computers in biology and medicine
May 27, 2024
BACKGROUND: Longitudinal data in health informatics studies often present challenges due to sparse observations from each subject, limiting the application of contemporary deep learning for prediction. This issue is particularly relevant in predictin...
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