Pediatrics

Can Machine Learning Algorithms use Contextual Factors to Detect Unwarranted Clinical Variation from Electronic Health Record Encounter Data during the Treatment of Children Diagnosed with Acute Viral Pharyngitis

Rationale, Aims and Objectives: Unwarranted clinical variation (UCV) in patient care often arises from contextual factors and contributes to increased costs, unnecessary treatments, and deviations from evidence-based practice. Detecting UCV is challenging due to the complexity of care decisions. Current approaches rely on centralized data aggregation and mixed-effects regression, which estimate re...

Crop-OCT: a Fully Integrated Imageomics Pipeline to Identify Regional and Focal Retinopathy in Murine Models

Imageomics uses machine learning to accelerate our understanding of biological traits and human disease processes. Some of the earliest imageomics applications used deep learning to assess human diseases. For example, retinal fundus images were analyzed to diagnose diabetic retinopathy. The imaging modality optical coherence tomography (OCT) is widely used to diagnose and monitor the progression o...

Decomposing response inhibition: a POMDP model

Inhibition is a core cognitive control function whose competence is distributed across the population, with more extreme impairments in psychiatric co...

PathMoE: Interpretable Multimodal Interaction Experts for Pediatric Brain Tumor Classification

Accurate classification of pediatric central nervous system tumors remains challenging due to histological complexity and limited training data. While...

Extending 2D foundational DINOv3 representations to 3D segmentation of neonatal brain MR images

Precise volumetric delineation of hippocampal structures is essential for quantifying neurodevelopmental trajectories in pre-term and term infants, wh...

Quantification of the effects of single nucleotide variants in NKX2.1 transcription factor binding sites

Transcription factors recognise and bind specific DNA sequence patterns in promoters and enhancers thereby regulating gene expression. Variations in t...

A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments

Advances in automation, imaging, and artificial intelligence have enabled researchers to capture large volumes of high-quality plant data for understa...

Classification of Adolescent Drinking via Behavioral, Biological, and Environmental Features: A Machine Learning Approach with Bias Control

In 2024, approximately 30% of U.S. adolescents reported having consumed alcohol at least once in their lifetime, with about 25% of these individuals e...

MovieTeller: Tool-augmented Movie Synopsis with ID Consistent Progressive Abstraction

With the explosive growth of digital entertainment, automated video summarization has become indispensable for applications such as content indexing, ...

Automated Model Discovery Based on COVID-19 Epidemiologic Data

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative appr...

Anatomical Accuracy of Generative AI for Congenital Heart Disease Illustrations: Gemini NanoBanana Versus ChatGPT Models in a Blinded Comparative Study

Background Generative artificial intelligence (AI) systems are increasingly used to produce medical illustrations for education; however, their anatom...

HD-TTA: Hypothesis-Driven Test-Time Adaptation for Safer Brain Tumor Segmentation

Standard Test-Time Adaptation (TTA) methods typically treat inference as a blind optimization task, applying generic objectives to all or filtered tes...

Harnessing AI and social media to understand real-world patient experiences in systemic lupus erythematosus

Objective: To apply large language models (LLMs) to Reddit posts referencing systemic lupus erythematosus (SLE) to identify patient-expressed unmet me...

Leveraging Expert Knowledge and Causal Structure Learning to Build Parsimonious Models of Acute Brain Dysfunction in the Pediatric Intensive Care Unit

Machine learning adoption in clinical decision support systems remains limited by concerns about transparency and robustness. Causal structure learnin...

TMS timed to interictal epileptiform discharges

Interictal epileptiform discharges (IEDs) are pathological hypersynchronous bursts of electrical brain activity that occur between seizures in patient...

Brain morphological pattern is associated with the presence, severity, and transition of transdiagnostic psychiatric disorders in preadolescents

Cognitive function, psychological processes, mental states, and behaviors are key dimensions of human subjective experience that separately relate to ...

Universal Image Immunization against Diffusion-based Image Editing via Semantic Injection

Recent advances in diffusion models have enabled powerful image editing capabilities guided by natural language prompts, unlocking new creative possib...

Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos

A substantial proportion (45\%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, with a particularly high burd...

A custom phenotypic profile for Fanconi anemia: Addressing gaps in existing disease annotations

Fanconi anemia (FA) is a rare genetic disorder of impaired DNA repair characterized by progressive bone marrow failure, congenital malformations, and ...

A radiation-free screening system for adolescent idiopathic scoliosis using deep learning on 3D back surface point clouds

Widespread screening for Adolescent Idiopathic Scoliosis (AIS) is critical for timely intervention but is currently constrained by the radiation risks...