Pediatrics

Optimizing mild traumatic brain injury (mTBI) triage: A comparative analysis of CEREBO® vs clinical prediction in reducing CT utilization and improving cost-effectiveness.

OBJECTIVE: Clinical Prediction Rules (CPR) such as the New Orleans Criteria (NOC), National Institute for Health and Clinical Excellence (NICE), and Canadian Computed Tomography Head Rules (CCHR) are standard triaging guidelines to determine the need for CT in mTBI. This study evaluates the effectiveness of CEREBO®, a machine learning-powered near infrared spectroscopy (NIRS) device, in triaging m...

Machine learning model predicts acute kidney injury in pediatric patients after cardiac surgery: a systematic review and meta-analysis.

BACKGROUND: Acute kidney injury (AKI) is a common complication following pediatric cardiac surgery, frequently leading to poor outcomes and even death in severe cases. Early prevention remains the primary intervention strategy. Studies have developed prediction models to identify at-risk children at an early stage. This study systematically evaluate existing AKI prediction models to support their ...

Machine learning-based study of breath sounds in children with asthma in remission and cough variant asthma.

BACKGROUND: Traditional auscultation, heavily dependent on the subjective judgment of physicians, can lead to variability in diagnoses. This study aim...

Wheeze detection in real-world pediatric care: AI applied to smartphone lung auscultation.

UNLABELLED: Smartphone-based lung auscultation, combined with artificial intelligence (AI), may offer a promising alternative for pediatric respirator...

Addressing the Treatment Gap for Pediatric Headache in Underserved Regions.

PURPOSE OF REVIEW: This review explores innovative strategies to address the treatment gap for pediatric headache disorders in underserved regions wor...

Research landscape, knowledge structure, and emerging frontiers of robot-assisted surgery for urolithiasis: a bibliometric and visualization analysis.

Robot-assisted techniques have been increasingly explored in urolithiasis, particularly in selected complex settings such as concomitant reconstructio...

Transfer learning for Multi-institutional Classification of Intussusception and Splenomegaly in Pediatric Abdominal Radiographs.

To address diagnostic delays in pediatric abdominal emergencies, this study aimed to develop and validate multi-institutional deep learning models for...

Lumbar, thoracic, and bilateral vertebral body tethering: do surgical outcomes differ?

PURPOSE: VBT is increasingly being used in the lumbar spine to preserve mobility. Surgeons have noted differences in complications and outcomes among ...

Comparing machine and deep learning models for pediatric anxiety classification using structured EHRs and area-based measures of health data.

OBJECTIVE: This retrospective, case-control study with internal validation evaluates the performance of machine learning (ML) and deep learning (DL) m...

Development and internal validation of an explainable machine-learning model to predict 3-year overall survival rate after radical cystectomy.

BACKGROUND: This study aimed to develop and internally validate an explainable machine-learning model using routinely available clinicopathologic and ...

Proceedings from the 2025 Midwest Pediatric Device Consortium showcase featuring software as a medical device.

On July 9, 2025, the Midwest Pediatric Device Consortium hosted its second showcase at the MidTown Collaboration Center in Cleveland, OH. This meeting...

Advances in Radiotherapy for Soft Tissue Sarcomas in 2025: A Review.

PURPOSE OF REVIEW: This review aims to synthesize and critically appraise the most impactful advances in radiotherapy (RT) for soft tissue sarcomas (S...

Natural Language Processing for Automated Classification of Cleft and Craniofacial Procedures From Operative Notes: Model Development and Feasibility Study.

BACKGROUND: The accurate classification of operative notes is essential for surgical outcomes research; however, CPT code classification is notoriousl...

Unlocking the Full Potential of Health Care Teams: How Artificial Intelligence Can Help.

Developing effective health care teams is critical to meet the rising complexity in patient care. However, optimizing team composition, interpersonal ...

Knowledge, attitudes, and practices toward artificial intelligence in medicine among Chinese physicians: A cross-sectional study from January to March 2024 with analysis of influencing factors.

BACKGROUND: As artificial intelligence (AI) transforms medicine, understanding physicians' knowledge, attitudes, and practices (KAP) toward AI is cruc...

Ambient Artificial Intelligence Scribes in Pediatric Hematology-Oncology: Early Implementation of DAX Copilot.

BACKGROUND: Ambient artificial intelligence scribes are increasingly integrated into electronic health records to reduce documentation burden, but lim...

Automated Detection of Patent Ductus Arteriosus in Pediatric Patients Using Doppler Ultrasonography Videos Based on a Transformer Model.

Patent ductus arteriosus (PDA) is a common congenital heart defect that requires timely and accurate detection to guide clinical management. Although ...

Computational Model for Predicting Optimal Clinical Intervention in Pre-Operative Neonates with Transposition of the Great Arteries.

PURPOSE: Transposition of the Great Arteries (TGA) is a congenital heart defect characterized by the abnormal positioning of the pulmonary artery over...

[Artificial intelligence in pediatric ophthalmology: applications and perspectives].

Pediatric visual health is not only inextricably linked to an individual's lifelong cognitive development and quality of life, but also constitutes a ...

Framing artificial intelligence in Chilean digital press before and after the launch of ChatGPT: From concern to optimism.

This article examines the evolution of AI framing in Chilean national digital media before and after the launch of ChatGPT in 2022. Through topic mode...