AIMC Topic: Infant, Premature
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TwinEDA: a sustainable deep-learning approach for limb-position estimation in preterm infants' depth images.
Medical & biological engineering & computing
Nov 28, 2022
Early diagnosis of neurodevelopmental impairments in preterm infants is currently based on the visual analysis of newborns' motion patterns by trained operators. To help automatize this time-consuming and qualitative procedure, we propose a sustainab...
Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities.
Pediatric research
Jul 29, 2022
The rise of machine learning in healthcare has significant implications for paediatrics. Long-term conditions with significant disease heterogeneity comprise large portions of the routine work performed by paediatricians. Improving outcomes through d...
Machine learning methods to predict attrition in a population-based cohort of very preterm infants.
Scientific reports
Jun 22, 2022
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...
Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review.
Pediatric research
May 31, 2022
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...
Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation.
Computer methods and programs in biomedicine
May 10, 2022
BACKGROUND AND OBJECTIVE: Bronchopulmonary dysplasia is a common respiratory disease in premature infants. The severity is diagnosed at the 56th day after birth or discharge by analyzing the clinical indicators, which may cause the delay of the best ...
Camera fusion for real-time temperature monitoring of neonates using deep learning.
Medical & biological engineering & computing
May 3, 2022
The continuous monitoring of vital signs is a crucial aspect of medical care in neonatal intensive care units. Since cable-based sensors pose a potential risk for the immature skin of preterm infants, unobtrusive monitoring techniques using camera sy...
Ultrasound Lung Image under Artificial Intelligence Algorithm in Diagnosis of Neonatal Respiratory Distress Syndrome.
Computational and mathematical methods in medicine
Mar 27, 2022
In order to analyze the application of ultrasonic lung imaging diagnosis model based on artificial intelligence algorithm in neonatal respiratory distress syndrome (NRDS), an ultrasonic lung imaging diagnosis model based on a deep residual network (D...
The Effects of Rso2 and PI Monitoring Images on the Treatment of Premature Infants Based on Deep Learning.
Computational and mathematical methods in medicine
Feb 22, 2022
In recent years, due to the combined effects of individual behavior, psychological factors, environmental exposure, medical conditions, biological factors, etc., the incidence of preterm birth has gradually increased, so the incidence of various comp...
Evaluation of Maturation in Preterm Infants Through an Ensemble Machine Learning Algorithm Using Physiological Signals.
IEEE journal of biomedical and health informatics
Jan 17, 2022
This study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional maturational age (FMA), using a machine learning model. We propose that the FMA, and its deviation from...
Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort.
Scientific reports
Nov 16, 2021
Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample popul...
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