Pediatrics

Comparable Outcomes in Pediatric and Adult Patients With Sudden Sensorineural Hearing Loss.

OBJECTIVE: To identify prognostic factors for unilateral idiopathic sudden sensorineural hearing los...

Discussion of Predicting Pediatric Surgical Case Duration Using Machine Learning: Leveraging Team Dynamics and Case Flow Attributes.

Automatic radiation-free evaluation of Cobb angle for spinal curvature based on fringe projection profilometry and deep learning technology.

OBJECTIVE: Pediatric scoliosis is the most prevalent spinal disorder, often leading to abnormal curv...

[Emphasizing evolution of key technological elements in reconstructive surgery].

Reconstructive surgery is fundamentally dedicated to restoring tissues and organs damaged by trauma,...

Pediatric diabetes prediction using machine learning.

Diabetes is a chronic condition that affects a substantial portion of the global population and is l...

Cross-well machine learning prediction of sonic logs in Newfoundland and Labrador.

Predicting compressional slowness (DTCO) from non-sonic logs can reduce acquisition cost, fill data ...

Complex Interactions Between Stress, Nutrition, Gut Microbiota, and Infectious Diseases and Their Impact on Health in Global Conflicts: A Narrative Review.

Following the global recovery from the COVID-19 pandemic, wars and conflicts have escalated to level...

PedIAtric Surgery: Artificial Intelligence and the new horizon in Science.

The Future of Critical Care.

The practice of caring for the critically ill has changed dramatically over the past 75 years, and t...

Artificial intelligence in the diagnosis and prognosis of pediatric bacterial pneumonia: current advances and challenges.

PURPOSE OF REVIEW: The clinical presentation of pediatric bacterial pneumonia often overlaps with th...

Bridging Nanostructure Growth and Gas-Sensing Kinetics in Metal-Functionalized Graphene Using Machine-Learned Molecular Dynamics.

Graphene functionalized with catalytic transition metals offers high-performance chemiresistive gas ...

PODiaCarD: a prototype of a digital twin platform for the management of pediatric obesity and related cardiometabolic complications.

UNLABELLED: Childhood obesity is the main driver of early metabolic risk, predisposing to cardiovasc...

Machine learning in the development and application of patient-reported outcome measures (PROMs) for surgical patients: a systematic review.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into ...

ChatGPT-guided translation of pediatric emergency department discharge instructions.

BACKGROUND: Language barriers in pediatric emergency medicine discharge instructions can impact pati...

Artificial Intelligence and the Potential Transformation of Mental Health.

IMPORTANCE: The potential of tools using artificial intelligence (AI) to address the many challenges...

Developing a Suicide Risk Prediction Algorithm Using Electronic Health Record Data in Mental Health Care: Real-World Case Study.

BACKGROUND: Artificial intelligence (AI) offers potential solutions to address the challenges faced ...

Leveraging machine learning for hemodynamic phenotyping in pediatric continuous renal replacement therapy-toward precision monitoring.

Individuation of predictive parameters of posterior cranial fossa decompression in pediatric patients with Chiari I malformation and autism spectrum disorder.

PURPOSE: There are no specific guidelines for posterior cranial fossa decompression (PCFD) in asympt...

Identification of modifiable plasma protein markers of cardiometabolic risk in children and adolescents with obesity.

Pediatric obesity is linked to multi-organ inflammation and an increased risk of cardiometabolic and...

The Role of AI in Improving Digital Wellness Among Older Adults: Comparative Bibliometric Analysis.

BACKGROUND: Advances in artificial intelligence (AI) have revolutionized digital wellness by providi...

Segmentation of the Upper Airway using Deep learning - nnUNet.

OBJECTIVE: This study addresses these challenges by developing a robust and efficient deep learning ...