Pediatrics

Interpretable Machine Learning Model for Survival Prediction in Pediatric Adrenocortical Tumors.

PURPOSE: Pediatric adrenocortical tumors (pACTs) are rare and clinically heterogeneous. Existing risk stratification systems rely on fixed thresholds and linear assumptions, which may limit their prognostic accuracy-particularly for nonmetastatic, locally advanced cases. We aimed to develop an interpretable machine learning (ML) model for individualized survival prediction using only routine clini...

Machine learning models for predicting hospital admission in pediatric emergency departments: A systematic review.

CONTEXT: Pediatric Emergency Departments (PEDs) face overcrowding partially due to delayed hospital admission decision. Machine Learning (ML) models could early predict it. OBJECTIVE: To systematically review and critically appraise the development, validation, quality, risk of bias, and applicability of ML models for predicting PEDs hospital admission. METHODS: PubMed, Cochrane, Web of Science an...

Assessing public interest in artificial intelligence in dermatology: A Google Trends analysis.

Artificial intelligence (AI) is rapidly transforming dermatology, particularly through diagnostic imaging and enhancing patient management. Despite ex...

Artificial intelligence-driven clinical decision support systems to assist healthcare professionals and people with diabetes in Europe at the point of care: a Delphi-based consensus roadmap.

The use of artificial intelligence (AI) to improve the diagnosis, assessment and treatment of people with diabetes has the potential to drive a paradi...

Novel growth pattern-specific digital marker of TILs improves stratification of lung adenocarcinoma patients.

Lung adenocarcinoma (LUAD) is one of the most prevalent forms of cancer and continues to be associated with high mortality rates, despite recent advan...

Non-Destructive Quantification of Mycelial Biocomposite Growth Over Time.

Mycelial biocomposites are sustainable alternatives to nonbiodegradable materials in building and packaging. Efficient manufacturing requires accurate...

[Interdisciplinary, research-based teaching format for participatory technology development in nursing: a mixed methods study].

Interdisciplinary, research-based teaching format for participatory technology development in nursing: a mixed methods study Abstract: Background: Dig...

Using conversational AI to reduce science skepticism.

Mistrust of the scientific consensus around issues such as climate change and vaccination is mainstream, compromising our ability to respond to existe...

Targeted inhibition of colorectal cancer by nano-dihydroartemisinin liposomes: Insights into the disruption of ABCG2 and BCL2 leading to induction of oxidative stress and metabolic disorder.

PURPOSE: Colorectal cancer is an aggressive malignancy characterized by significant drug resistance and a complex tumor microenvironment. Nano-dihydro...

Risk prediction models for adverse drug reactions in pediatrics: a scoping review.

INTRODUCTION: Predictive models play a critical role in enhancing medication safety in clinical practice. While multiple models for adverse drug react...

Individual differences in effects of stressful life events on childhood ADHD: genetic, neural, and familial contributions.

BACKGROUND: This study elucidates the intricate relationship between stressful life events and the development of ADHD symptoms in children, acknowled...

Digital Eye: Deep Learning for Detecting Physeal Fractures of the Pediatric Distal Radius.

BACKGROUND: Distal radial fractures (DRFs) are some of the most common pediatric injuries, often involving the physis. Diagnostic accuracy can be chal...

Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.

Artificial intelligence (AI) is rapidly transforming the delivery of kidney care through predictive analytics, machine learning, deep learning, and ge...

Reliability of Multimodal AI for Assessing Preclinical Stainless Steel Crown Preparations: A Comparative Study With Human Experts.

BACKGROUND: Artificial intelligence presents the potential to enhance consistency and objectivity in preclinical pediatric dentistry assessments. AIM:...

Anterior segment optical coherence tomography in uveitis: A comprehensive review of clinical applications, diagnostic insights, and future directions.

Anterior segment optical coherence tomography (AS-OCT) is emerging as an essential tool in the diagnosis and monitoring of uveitis. Offering noninvasi...

Triplet longitudinal masked autoencoder for predicting individualized functional connectome development during infancy.

Brain functional connectivity (FC) constructed from resting-state functional MRI (rs-fMRI) is the predominant method for studying brain functional org...

Artificial intelligence methods in gestational diabetes mellitus prediction: A systematic literature review.

Gestational diabetes mellitus (GDM) is the most common metabolic disorder in pregnancy, posing risks to both maternal and neonatal health. Artificial ...

Training a Bilingual Artificial Intelligence Model on Pediatric Orthopaedic Resources to Enhance the Readability of Patient Education Materials.

BACKGROUND: Low health literacy affects nearly one-third of adults in the United States, and almost 68 million Americans speak a language other than E...

AI as a Novel Digital Stressor in Adolescent Psychosis: Clinical and Ethical Implications.

ObjectiveWith the rapid adoption of artificial intelligence (AI) technologies by adolescents, the impact on their mental health is of critical concern...

Investigating the potential of combining Raman spectroscopy and machine learning for gestational age classification in rat cervical tissue.

Spontaneous preterm birth (SPB) is a leading cause of neonatal morbidity and mortality worldwide. It occurs when the uterine cervix (UC) opens prematu...