Pediatrics

Latest AI and machine learning research in pediatrics for healthcare professionals.

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Predicting Substance Use and Psychotic-Like Experiences in Adolescents

Adolescence is a critical developmental window for the emergence of substance use and psychosis-spec...

Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs

Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnos...

Synthetic Data Alone is Enough? Rethinking Data Scarcity in Pediatric Rare Disease Recognition

Children with rare genetic diseases often exhibit distinctive facial phenotypes, yet developing comp...

DeepBioGS: a hybrid framework for integrating crop growth modelling with genomic prediction through neural networks

Ensuring global food security under rapid climate change demands accelerated genetic gain and breedi...

Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence

Adolescence is an important developmental period during which there are diverse changes in the brain...

Satellite imagery encodes features predictive of regional mortality and life expectancy

Background Increasingly accessible satellite imagery provides scalable measures of the built and nat...

Predicting Intensive Care Readmission Among Hospitalized Children

Objective: Readmissions to the PICU are associated with increased morbidity and mortality. A predict...

VISTA: Variance-Gated Inter-Sequence Test-Time Adaptation for Multi-Sequence MRI Segmentation

Deploying multi-sequence magnetic resonance imaging (MRI) segmentation models to new clinical enviro...

DynoSys 2.0: Graph-Based Modeling of Dynamic Risk States and System Transitions in Human Behaviours Development

Human behavioral and mental health outcomes arise from interactions among genetic, environmental, an...

Transferable Transcriptional Topic Modeling Traces Medulloblastoma Subtypes to Distinct Cerebellar Developmental States

Single-cell transcriptomics transformed our understanding of cellular heterogeneity, yet cross-datas...

Enhanced processing of cartoons in infant visual cortex

Developing sensory systems may have heightened sensitivity to exaggerated features that emphasize di...

Cortical reconstruction and anatomical parcellation of high-resolution multi-modal postmortem ex vivo MRI of the human infant brain

High-resolution postmortem (ex vivo) magnetic resonance imaging enables detailed examination of brai...

Automated Brain and CSF Volume Assessment in Infant Hydrocephalus Using Deep Learning

Accurate brain and cerebrospinal fluid (CSF) volume assessment is essential for pediatric hydrocepha...

Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy

Deep learning-based organs/structures-at-risk(OARs) auto-contouring models can improve radiotherapy ...

An electrocardiogram-based machine learning model for distinguishing complete Kawasaki disease.

Kawasaki disease (KD) is a systemic vasculitis in young children, and early diagnosis remains challe...

Early Detection of Rare Disease Using Hierarchical Set-to-Sequence Modeling of Structured Electronic Health Records

Rare diseases are characterized by heterogeneous, weak, and sparse phenotypic signals that emerge gr...

Learning the Language of the Microbiome with Transformers

Self-supervised pretraining has become central to biological machine learning, yet microbiome data r...

Optimizing Screening for Intrauterine Fetal Growth Restriction in Low-Resource Settings Using 2D Ultrasound: A Deep Learning Approach

Severe fetal growth restriction (sFGR) affects 5 to 10% of pregnancies worldwide and is a major cont...

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