Pediatrics

Refining the genetic landscape of anophthalmia and microphthalmia: a comprehensive framework with deep learning and updated gene panels

Anophthalmia and microphthalmia (A/M) are rare congenital eye disorders with a low molecular diagnosis rate, which limits clinical management and genetic counselling. Improved detection and interpretation of pathogenic variants is essential for advancing diagnosis and care in affected individuals. To improve the molecular diagnostic yield in A/M patients by refining the methodology of variant inve...

Adolescents with non-suicidal self-injury exhibit increased pain empathic neural reactivity and personal distress to physical but not affective pain

Non-suicidal self-injury (NSSI) in adolescents represents a critical public health issue. While symptomatic links between NSSI and alterations in pain and social processing have been established, changes in neural responses and everyday reactivity to others’ pain remain unknown. This pre-registered study examined pain empathic processing in unmedicated adolescents with NSSI (n=29) and healthy cont...

From Sequences to Strategies: Early Detection of New SARS-CoV-2 Variants via Genetic Distance to Reduce Hospitalizations

The COVID-19 pandemic highlighted the critical need for robust methods to monitor viral evolution and detect emerging variants of concern (VOCs). Trad...

Textbook-Level Medical Knowledge in Large Language Models: A Comparative Evaluation Using the Japanese National Medical Examination

This study aimed to evaluate the performance of four reasoning-enhanced large language models (LLMs)—GPT-5, Grok-4, Claude Opus 4.1, and Gemini 2.5 Pr...

The human milk microbiome varies by environmental factors and is associated with infant growth: findings from the IMiC Consortium

Human milk (HM) is a complex ecological matrix that connects mothers and infants to the surrounding environment, and promotes infant growth and health...

Wireless Colorimetric Multi-Biomarker Sensing to Enable Critical Neonatal Monitoring

Clinical monitoring in the most vulnerable patients such as newborns relies on invasive and costly procedures and/or wired sensor surveillance, increa...

Clinical evaluation of a natural language processing system for assisting structured diagnosis recording at the point of care: MiADE (Medical Information AI Data Extractor)

Structured recording of key information such as diagnoses is essential for safe, efficient patient care, but is currently done incompletely because it...

Candidate Correlates of Protection in the HVTN505 HIV-1 Vaccine Efficacy Trial Identified by Positive-Unlabeled Learning

With a goal of unveiling mechanisms by which vaccines can provide protection against HIV-1 acquisition, several studies have explored correlates of ri...

Deep learning-based prediction of cardiopulmonary disease in retinal images of premature infants

Bronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are leading causes of morbidity and mortality in premature infants. To determine whet...

Objective Assessment of Microperimetry Exam Using EEG Signals

To test the hypothesis that deep learning can decode single-trial cortical responses from electroencephalography (EEG) to individual, long-duration mi...

Identifying High-Risk Adolescents for Mental Health Difficulties: A Machine Learning Analysis of the Health Behaviour in School-aged Children Study Across 46 Countries

Adolescent mental health represents a global public health crisis, yet traditional surveillance methods lack the scalability and predictive power need...

Multimodal Machine Learning for Diagnosis of Multiple Sclerosis Using Optical Coherence Tomography in Pediatric Cases

Identifying MS in children early and distinguishing it from other neuroinflammatory conditions of childhood is critical, as early therapeutic interven...

Reducing Inequalities Using an Unbiased Machine Learning Approach to Identify Births with the Highest Risk of Preventable Neonatal Deaths

Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mort...

Predicting Vaping Cessation in Young Adults: A Machine Learning and Explainable Artificial Intelligence (XAI) Approach to Public Health Intervention

The public health impact of vaping in the United States reflects a complex balance of potential benefits and emerging risks. While e-cigarettes can su...

A machine learning model for prediction of early-onset neonatal sepsis in low- and middle-income countries: Development and validation study

Early-onset sepsis (EOS), which occurs within the first 72 hours of life, can often be fatal for neonates. Machine learning (ML) models demonstrate pr...

A Systematic Process for Assessing Fitness-for-Purpose of Health Outcomes for Computable Phenotyping with Electronic Health Record Data

Information from electronic health records (EHRs) may be incorporated into computable phenotype algorithms in efforts to overcome inaccuracies of algo...

Appendix300: A multi-institutional laparoscopic appendectomy video dataset for computational modeling tasks

The limited availability of diverse and representative training data poses a critical barrier to the development of clinically relevant computational ...

Childhood Maltreatment and Risk for Illicit Substance Use: Evidence for Mid-Adolescence as a Sensitive Exposure Period

Childhood maltreatment is a well-established risk factor for substance misuse. However, it remains unclear whether risk for specific illicit substance...

Interpretable machine learning applied to high-dimensional salivary proteomics accurately classifies pediatric inflammatory bowel diseases

Inflammatory bowel diseases (IBD), including Crohn’s disease (CD), ulcerative colitis (UC), and IBD-unclassified (IBD-U), are chronic inflammatory dis...

A Multi-Task Deep Learning Model for Pediatric Echocardiography Analysis

Congenital heart defects afflict nearly 1% of all births worldwide. While deep learning algorithms have shown significant promise in automating and im...