Pediatrics

[Development and Application of Deep Learning-Based Model for Quality Control of Children Pelvic X-Ray Images].

OBJECTIVE: A deep learning-based method for evaluating the quality of pediatric pelvic X-ray images ...

From tradition to innovation: conventional and deep learning frameworks in genome annotation.

Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCO...

Clinical effects of a novel deep learning-based rehabilitation application on cardiopulmonary function, dynamic and static balance, gait function, and activities of daily living in adolescents with hemiplegic cerebral palsy.

BACKGROUND: Adolescents with hemiplegic cerebral palsy undergo conventional physical therapy (CPT) t...

An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children.

STUDY OBJECTIVES: Although sleep is frequently disrupted in the pediatric intensive care unit, it is...

Artificial Intelligence in Pediatric Respiratory Diseases: Current Status and Future Promises.

Beyond the aspirational: creating the future of health care in Australia.

The context of health care in Australia is shifting very rapidly; more chronic diseases, budgetary s...

Development of a pharmaceutical science systematic review process using a semi-automated machine learning tool: Intravenous drug compatibility in the neonatal intensive care setting.

Our objective was to establish and test a machine learning-based screening process that would be app...

Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm.

OBJECTIVE: Preterm infants are at high risk of neuromotor disorders. Recent advances in digital tech...

Utilization of Machine Learning Approaches to Predict Mortality in Pediatric Warzone Casualties.

BACKGROUND: Identification of pediatric trauma patients at the highest risk for death may promote op...

Current Applications of Artificial Intelligence for Pediatric Dentistry: A Systematic Review and Meta-Analysis.

To systematically evaluate artificial intelligence applications for diagnostic and treatment planni...

ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from ...

Modeling energy partition patterns of growing pigs fed diets with different net energy levels based on machine learning.

The objectives of this study were to evaluate the energy partition patterns of growing pigs fed diet...

Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis.

MOTIVATION: Pediatric kidney disease is a widespread, progressive condition that severely impacts gr...

Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement.

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visu...

Human-computer interactions and compassionate healthcare: A Wizard of Oz study using a self-administered AI-assisted cognitive assessment

Artificial intelligence (AI) is increasingly transforming healthcare, however, the interaction betwe...

Using Large Language Models for sentiment analysis of health-related social media data: empirical evaluation and practical tips.

Health-related social media data generated by patients and the public provide valuable insights into...

Impact of AI Decision Support on Clinical Experts' Radiographic Interpretation of Adamantinomatous Craniopharyngioma.

This research explores the integration of Artificial Intelligence (AI) into clinical decision-making...

Developing an AI Tool to Derive Social Determinants of Health for Primary Care Patients: Qualitative Findings From a Codesign Workshop.

PURPOSE: Information about social determinants of health (SDOH) is essential for primary care clinic...

Family Medicine Must Prepare for Artificial Intelligence.

Artificial Intelligence (AI) is poised to revolutionize family medicine, offering a transformative a...

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

Primary care physicians are likely both excited and apprehensive at the prospects for artificial int...

Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection.

OBJECTIVES: Despite nearly universal prenatal ultrasound screening programs, congenital heart defect...