Pediatrics

Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins.

With the rapid development of information technology, great changes have taken place in the way of managing, analyzing, and using data in all walks of life. Using deep learning algorithm for data analysis in the field of medicine can improve the accuracy of disease recognition. The purpose is to realize the intelligent medical service mode of sharing medical resources among many people under the d...

Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease.

Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on the underlying biology and pathophysiology of a patient's condition, personalized medicine relies on digital biomarkers generated through alg...

The circular economy through the prism of machine learning and the YouTube video media platform.

The transition to a Circular Economy (CE) is rapidly gaining ground across countries and industries. It is the means of achieving more sustainable dev...

Free access via computational cloud to deep learning-based EEG assessment in neonatal hypoxic-ischemic encephalopathy: revolutionary opportunities to overcome health disparities.

In this issue of Pediatric Research, Kota et al. evaluate a novel monitoring visual trend using deep-learning - Brain State of the Newborn (BSN)- base...

The heart sound classification of congenital heart disease by using median EEMD-Hurst and threshold denoising method.

Heart sound signals are vital for the machine-assisted detection of congenital heart disease. However, the performance of diagnostic results is limite...

Identifying psychological predictors of SARS-CoV-2 vaccination: A machine learning study.

BACKGROUND: Major barriers to addressing SARS-CoV-2 vaccine hesitancy include limited knowledge of what causes delay/refusal of SARS-CoV-2 vaccination...

Transforming Health Care Landscapes: The Lever of Radiology Research and Innovation on Emerging Markets Poised for Aggressive Growth.

Advances in radiology are crucial not only to the future of the field but to medicine as a whole. Here, we present three emerging areas of medicine th...

Plastic particles and fluorescent brightener co-modify Chlorella pyrenoidosa photosynthesis and a machine learning approach predict algae growth.

Global release of plastics exerts various impacts on the ecological cycle, particularly on primary photosynthesis, while the impacts of plastic additi...

Application of ChatGPT in pediatric surgery and further issues that should be addressed: comment.

Deep learning-based automated bone age estimation for Saudi patients on hand radiograph images: a retrospective study.

PURPOSE: In pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeut...

Artificial Intelligence for Early Detection of Pediatric Eye Diseases Using Mobile Photos.

IMPORTANCE: Identifying pediatric eye diseases at an early stage is a worldwide issue. Traditional screening procedures depend on hospitals and ophtha...

[Use of artificial intelligence in geographic atrophy in age-related macular degeneration].

The first regulatory approval of treatment for geographic atrophy (GA) secondary to age-related macular degeneration in the USA constitutes an importa...

Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.

Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transport...

Evaluation of Comfort Behavior Levels of Newborn by Artificial Intelligence Techniques.

BACKGROUND: One of the scales most frequently used in the evaluation of newborn comfort levels is the Neonatal Comfort Behavior Scale (NCBS). It is im...

Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning.

RATIONALE: More targeted management of severe acute pediatric asthma could improve clinical outcomes.

Artificial intelligence assisted diagnosis of chronic lymphocytic leukemia in a historic pediatric case using large language models.

An attention-based deep learning for acute lymphoblastic leukemia classification.

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrence...

A machine learning approach to predict in vivo skin growth.

Since their invention, tissue expanders, which are designed to trigger additional skin growth, have revolutionised many reconstructive surgeries. Curr...

Testing behaviour change with an artificial intelligence chatbot in a randomized controlled study.

Chatbots can effect large-scale behaviour change because they are accessible through social media, flexible, scalable, and gather data automatically. ...

Diagnostic Performance of Machine Learning-based Models in Neonatal Sepsis: A Systematic Review.

BACKGROUND: Timely diagnosis of neonatal sepsis is challenging. We aimed to systematically evaluate the diagnostic performance of sophisticated machin...