Latest AI and machine learning research in pediatrics for healthcare professionals.
Background Increasingly accessible satellite imagery provides scalable measures of the built and natural environment relevant to population health. However, whether such imagery can capture subnational variation in mortality and life expectancy remains unclear. We therefore assessed its predictive value for regional mortality and life expectancy across OECD regions. Methods We conducted an ecologi...
Objective: Readmissions to the PICU are associated with increased morbidity and mortality. A prediction model that can identify children at risk of readmission at the time of transfer can allow providers to intervene and potentially improve patient outcomes. The objective of this study was to derive and validate machine learning models to predict PICU readmission at the time of transfer. Design: R...
Deploying multi-sequence magnetic resonance imaging (MRI) segmentation models to new clinical environments is challenging due to variations in scanner...
Decision-making circuits are modulated across life stages (e.g. juvenile, adolescent, or adult), as well as on the shorter timescale of reproductive c...
Human behavioral and mental health outcomes arise from interactions among genetic, environmental, and neurobiological systems. Existing frameworks oft...
The US Food and Drug Administration (FDA) maintains a public list of artificial intelligence and machine learning (AI/ML)-enabled medical devices that...
Single-cell transcriptomics transformed our understanding of cellular heterogeneity, yet cross-dataset comparison remains fundamentally limited by bat...
Developing sensory systems may have heightened sensitivity to exaggerated features that emphasize diagnostic information, as shown by the benefits of ...
High-resolution postmortem (ex vivo) magnetic resonance imaging enables detailed examination of brain anatomy at spatial scales not achievable in vivo...
Accurate brain and cerebrospinal fluid (CSF) volume assessment is essential for pediatric hydrocephalus management. Current clinical practice relies o...
Deep learning-based organs/structures-at-risk(OARs) auto-contouring models can improve radiotherapy workflows, but models trained on adult data often ...
Kawasaki disease (KD) is a systemic vasculitis in young children, and early diagnosis remains challenging when clinical features are incomplete or ove...
Rare diseases are characterized by heterogeneous, weak, and sparse phenotypic signals that emerge gradually across longitudinal clinical visits, makin...
Self-supervised pretraining has become central to biological machine learning, yet microbiome data remains comparatively underexplored in terms of bot...
Severe fetal growth restriction (sFGR) affects 5 to 10% of pregnancies worldwide and is a major contributor to perinatal morbidity and mortality, part...
Recovering scene color from images captured in scattering media is a fundamental inverse problem in optical imaging. Yet the problem is intrinsically ...
Background: Most studies seeking to identify youth at increased risk for depression have developed prediction models using a limited set of risk facto...
Automated pediatric electrocardiogram (ECG) diagnosis remains challenging because models trained predominantly on adult data suffer from substantial c...
Second generation antiandrogens, such as enzalutamide, are commonly prescribed to treat advanced prostate cancer. However, enzalutamide resistant pros...
Audio-based stuttering systems to date have been trained for detection -- what disfluency is present now -- leaving prediction, the capability needed ...