Pediatrics

INTERPRETABLE DEEP LEARNING APPLIED TO FLUORESCENCE CONFOCAL MICROSCOPY FOR INTRAOPERATIVE TISSUE ASSESSMENT IN PEDIATRIC SURGICAL ONCOLOGY.

BACKGROUND: In pediatric surgical oncology, intraoperative tissue assessment is limited by small specimen size and the absence of real time histopathology. Ex vivo fluorescence confocal microscopy (FCM) provides rapid histology like imaging of fresh tissue, yet interpretation relies on expert visual assessment. We tested the hypothesis that deep learning applied directly to ex vivo FCM images enab...

Quantifying the Intensity of Online Social Support via Large Language Model-Based Evidence Extraction: Development and Validation Study.

BACKGROUND: Online social support, the interaction among individuals in which one helps another during difficult situations through online platforms such as online forums or social media, has proliferated as a vital tool for personal mental health care. Despite the growing usage and importance of online social support, prior studies have mainly focused on either understanding the characteristics o...

AI-driven Assessment of Vertebral Rotation in Adolescent Idiopathic Scoliosis Using Weight-bearing Cone-beam CT.

RATIONALE AND OBJECTIVES: To evaluate an artificial intelligence (AI)-based algorithm for quantifying vertebral rotation angles on weight-bearing cone...

Biological domain shift and statistical nesting concerns in generative AI-based spatial tumor growth prediction for pediatric diffuse midline glioma.

BACKGROUND: Laslo et al. recently reported a guided denoising diffusion implicit model for spatial tumor growth prediction on magnetic resonance imagi...

Predicting the Future of Craniofacial Surgery: AI-Enabled Forecasting of Craniofacial Surgery Research and Future Clinical Priorities.

BACKGROUND: Artificial intelligence (AI) is increasingly used in clinical decision support and perioperative planning, yet its role in anticipating th...

Correlates of muscle strengthening exercise in adolescents: A machine learning based analysis.

BACKGROUND: Muscle-strengthening exercise (MSE) is a critical component of adolescent health, yet its correlates remain less understood than those of ...

A scoping review on the application of new technology in the screening and diagnosis of adolescent idiopathic scoliosis.

INTRODUCTION: Adolescent Idiopathic Scoliosis (AIS) affects 2-4% of adolescents globally. Current screening methods face challenges, including high fa...

Machine Learning-Based Classification of Neuroblastoma Risk Groups and MYCN Status.

BACKGROUND: Artificial intelligence (AI)-based methods have demonstrated considerable promise in identifying histological features in whole slide imag...

Machine learning-based prediction of postoperative restenosis risk following direct repair of paediatric coarctation of the aorta.

OBJECTIVES: Coarctation of the aorta is a congenital cardiovascular disease with focal aortic luminal narrowing, and paediatric patients face a high p...

Time-Domain HRV Metrics as Predictors of Concussion Recovery in Adolescents: A Boosted Tree Approach.

BACKGROUND: Concussions in adolescents, especially in sports, represent a major public health issue due to prolonged recovery and diagnostic challenge...

The Pediatric Pulmonary Hypertension International Risk Score: A Prediction Model for Outcomes Using Machine Learning.

BACKGROUND: Risk prediction is fundamental to pulmonary hypertension (PH) guideline-based care, yet pediatric-specific risk prediction models remain l...

A Machine Learning Pipeline to Analyze Global Sentiment and Factors Influencing Retinoblastoma Treatment Hesitancy: Observational Infodemiology Study.

BACKGROUND: The use of social media in cancer research, patient support, and information sharing has been well documented. OBJECTIVE: Using retinoblas...

Methods of Evaluating Large Language Model-Based Health Care Applications Used by Nonprofessionals: Protocol for a Scoping Review.

BACKGROUND: Large language models (LLMs) are increasingly used in health care by nonprofessionals (ie, individuals without formal training in health-r...

Correction: Hypoxemia prediction in pediatric patients under general anesthesia using machine learning: A retrospective observational study and external validation.

[This corrects the article DOI: 10.1371/journal.pone.0339276.].

In Vitro liposome release profile prediction using explainable machine learning approaches.

Formulation features and test settings influence liposomal in vitro release (IVR) profiles, yet it is challenging to examine these multivariable assoc...

From phenoscope to GreenLab model of Arabidopsis to decipher genotype and treatment effects.

Arabidopsis thaliana leaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabl...

Real-world analysis of depression-related adverse events associated with the adsorbed anthrax vaccine: integrating pharmacovigilance signals, machine learning risk prediction, and transcriptomic mechanisms.

BACKGROUND: Anthrax remains a life-threatening zoonotic disease in resource-limited settings. Adsorbed anthrax vaccine (AVA, BioThrax) is the only Uni...

Broad environmental adaptation is associated with root anatomical phenotypes in maize landraces: an in silico study.

BACKGROUND AND AIMS: Root phenotypes contribute to environmental adaptation. We hypothesized that root phenotypes of maize (Zea mays L. ssp. mays) lan...

Behavioral Mechanisms of a Digital Health Intervention for Self-Management in Type 2 Diabetes Mellitus: Prospective Longitudinal Cohort Study.

BACKGROUND: Sustaining self-management is critical for optimizing clinical outcomes in individuals with type 2 diabetes mellitus (T2DM). Although digi...

Preferences for AI-Enabled Health Care Technologies: Systematic Review of Discrete Choice Experiments and Reporting Quality Assessment Using the DIRECT Checklist.

BACKGROUND: AI is increasingly being integrated into health care, making it important to understand stakeholder preferences for AI-enabled technologie...