AIMC Topic: Infant, Newborn

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Prediction of preterm birth using artificial intelligence: a systematic review.

Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology
Preterm birth is the leading cause of neonatal death. It is challenging to predict preterm birth. We elucidated the state of artificial intelligence research on the prediction of preterm birth, clarifying the predictive values and accuracy. We perfor...

Development and Validation of a Deep Learning Model to Predict the Occurrence and Severity of Retinopathy of Prematurity.

JAMA network open
IMPORTANCE: Retinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. Prediction of ROP before onset holds great promise for reducing the risk of blindness.

Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review.

Pediatric research
BACKGROUND: Machine learning has been attracting increasing attention for use in healthcare applications, including neonatal medicine. One application for this tool is in understanding and predicting neurodevelopmental outcomes in preterm infants. In...

HGSORF: Henry Gas Solubility Optimization-based Random Forest for C-Section prediction and XAI-based cause analysis.

Computers in biology and medicine
A stable predictive model is essential for forecasting the chances of cesarean or C-section (CS) delivery, as unnecessary CS delivery can adversely affect neonatal, maternal, and pediatric morbidity and mortality, and can incur significant financial ...

Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0-6 Years.

International journal of environmental research and public health
Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud techno...

Predicting risks of low birth weight in Bangladesh with machine learning.

PloS one
BACKGROUND AND OBJECTIVE: Low birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to determine the risk factors of ...

NeoAI 1.0: Machine learning-based paradigm for prediction of neonatal and infant risk of death.

Computers in biology and medicine
BACKGROUND: The Neonatal mortality rate in the United States is 3.8 deaths per 1000 live births, which is comparably higher than other nations.

Deep learning of birth-related infant clavicle fractures: a potential virtual consultant for fracture dating.

Pediatric radiology
BACKGROUND: In infant abuse investigations, dating of skeletal injuries from radiographs is desirable to reach a clear timeline of traumatic events. Prior studies have used infant birth-related clavicle fractures as a surrogate to develop a framework...

[The role of bile acid measurement in the management of intrahepatic cholestasis of pregnancy].

Orvosi hetilap
Introduction: Intrahepatic cholestasis of pregnancy complicates 1% of pregnancies. It increases the risk of severe fetal complications significantly, including preterm delivery and stillbirth. Objective: To summarize our experience with serum total b...

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides.

Analytical chemistry
The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tan...