Pediatrics

AI for chronic pain in children: a powerful resource.

Given the lack of scientific evidence, chronic pain represents an arduous challenge, especially in the pediatric field. In this complex scenario, artificial intelligence (AI) could support diagnosis, therapy, and research. However, the great potential of AI must be combined with the protection of data and the most fragile patients.

A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care.

The IoT Smart Cradle for Baby Monitoring System & Infant Care is introduced as an innovative solution to address critical gaps in contemporary infant care. This system integrates Internet of Things (IoT) technology, machine learning, and smart automation to offer a safer, more responsive, and comfortable environment for babies. A significant challenge in current infant care is the limitations of t...

FOLIAGE: Towards Physical Intelligence World Models Via Unbounded Surface Evolution

Physical intelligence -- anticipating and shaping the world from partial, multisensory observations -- is critical for next-generation world models....

3DGEER: Exact and Efficient Volumetric Rendering with 3D Gaussians

3D Gaussian Splatting (3DGS) marks a significant milestone in balancing the quality and efficiency of differentiable rendering. However, its high ef...

Model Immunization from a Condition Number Perspective

Model immunization aims to pre-train models that are difficult to fine-tune on harmful tasks while retaining their utility on other non-harmful task...

Dual-Task Graph Neural Network for Joint Seizure Onset Zone Localization and Outcome Prediction using Stereo EEG

Accurately localizing the brain regions that triggers seizures and predicting whether a patient will be seizure-free after surgery are vital for sur...

Deep Retrieval at CheckThat! 2025: Identifying Scientific Papers from Implicit Social Media Mentions via Hybrid Retrieval and Re-Ranking

We present the methodology and results of the Deep Retrieval team for subtask 4b of the CLEF CheckThat! 2025 competition, which focuses on retrievin...

A posteriori error estimates and adaptivity for locally conservative methods. Inexpensive implementation and evaluation, polytopal meshes, iterative linearization and algebraic solvers, and applications to complex porous media flows

A posteriori estimates give bounds on the error between the unknown solution of a partial differential equation and its numerical approximation. We ...

Can Modern NLP Systems Reliably Annotate Chest Radiography Exams? A Pre-Purchase Evaluation and Comparative Study of Solutions from AWS, Google, Azure, John Snow Labs, and Open-Source Models on an Independent Pediatric Dataset

General-purpose clinical natural language processing (NLP) tools are increasingly used for the automatic labeling of clinical reports. However, inde...

Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.

Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has...

Test-Time Immunization: A Universal Defense Framework Against Jailbreaks for (Multimodal) Large Language Models

While (multimodal) large language models (LLMs) have attracted widespread attention due to their exceptional capabilities, they remain vulnerable to...

Imaging biomarkers for detection and longitudinal monitoring of ventricular abnormalities from birth to childhood.

This narrative review examines the use of imaging biomarkers for diagnosing and monitoring hydrocephalus from birth through childhood. Early detection...

Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study.

BACKGROUND: Fetal growth restriction (FGR) is a common complication of preeclampsia. FGR in patients with preeclampsia increases the risk of neonatal-...

Clinical and economic effectiveness of Schroth therapy in adolescent idiopathic scoliosis: insights from a machine learning- and active learning-based real-world study.

BACKGROUND: Adolescent idiopathic scoliosis (AIS) is a prevalent musculoskeletal condition affecting approximately 2-3% of the adolescent population. ...

Detecting microcephaly and macrocephaly from ultrasound images using artificial intelligence.

BACKGROUND: Microcephaly and macrocephaly, which are abnormal congenital markers, are associated with developmental and neurologic deficits. Hence, th...

Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis.

OBJECTIVE: To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis ...

Prospective study using artificial neural networks for identification of high-risk COVID-19 patients.

The COVID-19 pandemic caused a major public health crisis, with severe impacts on global health and the economy. Machine learning (ML) has been crucia...

RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs

The rapid advancement of native multi-modal models and omni-models, exemplified by GPT-4o, Gemini, and o3, with their capability to process and gene...

Edge-Dependent Step-Flow Growth Mechanism in β-GaO (100) Facet at the Atomic Level.

Homoepitaxial step-flow growth of high-quality β-GaO thin films is essential for the advancement of high-performance GaO-based devices. In this work, ...

Evaluating machine- and deep learning approaches for artifact detection in infant EEG: classifier performance, certainty, and training size effects.

Electroencephalography (EEG) is essential for studying infant brain activity but is highly susceptible to artifacts due to infants' movements and phys...