Pediatrics

A prospective study for the examination of peripheral blood smear samples in pediatric population using artificial intelligence.

BACKGROUND/AIM: Peripheral blood smear (PBS) and bone marrow aspiration are gold standards of manual...

Interpretable machine learning-based insights into early-life endocrine disruptor exposure and small vulnerable newborns.

Early-life exposure to endocrine-disrupting chemicals (EDCs) may contribute to small vulnerable newb...

Stability of Jurkat cells during short-term liquid storage analyzed by flow imaging microscopy.

The viability of cell-based medicinal products (CBMPs) is a critical quality attribute and must be a...

Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis.

: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mor...

Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock.

This study aims to predict and diagnose pediatric septic shock through the screening of immune infil...

Comment on "Artificial Intelligence for the Diagnosis of Pediatric Appendicitis: A Systematic Review".

Perceived Barriers and Facilitators of Use of Artificial Intelligence in Eating Disorder Care: A Commentary on Linardon et al. (2025).

Artificial intelligence (AI) has the potential to revolutionize mental health care, including for ea...

Application of automatic image analysis using a Deep Learning Neural Network for assessing the growth of green algae containing carotenoids - importance for environment, health and aquaculture.

Using deep learning and neural networks enables us to greatly speed-up quantitative studies and prov...

Cell Wall-Based Machine Learning Models to Predict Plant Growth Using Onion Epidermis.

The plant cell wall (CW) is a physical barrier that plays a dual role in plant physiology, providing...

Machine learning insights for sustainable hydroponic cultivation and growth monitoring of allium cepa using smart hydro kit.

This research paper emphasizes the growing importance of Allium Cepa (Onions)-a medicinal plant, as ...

Development of an artificial intelligence-enhanced warfarin interaction checker platform.

Warfarin is a common anticoagulant drug for thrombo-prophylaxis in stroke and venous thromboembolism...

Genetic algorithm-optimized artificial neural network for multi-objective optimization of biomass and exopolysaccharide production by Haloferax mediterranei.

Microbial production of industrially important exopolysaccharide (EPS) from extremophiles has severa...

Machine learning-based prediction of vesicoureteral reflux outcomes in infants under antibiotic prophylaxis.

We aimed to investigate the independent outcome predictors of continuous antibiotic prophylaxis (CAP...

Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit.

pressure injuries are significant concern for ICU patients on mechanical ventilation. Early predicti...

The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review.

Artificial intelligence (AI) is rapidly gaining attention in radiology and cardiology for accurately...

Artificial intelligence system for predicting hand-foot skin reaction induced by vascular endothelial growth factor receptor inhibitors.

Hand-foot skin reaction (HFSR) is a common adverse effect of vascular endothelial growth factor rece...

Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023).

BACKGROUND: Despite the rapid growth of research in artificial intelligence/machine learning (AI/ML)...

Unveiling sex difference in factors associated with suicide attempt among Chinese adolescents with depression: a machine learning-based study.

BACKGROUND: Adolescents with depression are at heightened risk of suicide, with a distinct sex diffe...

Comment on "Artificial intelligence for the diagnosis of pediatric appendicitis: A systematic review".

Transforming neurodegenerative disorder care with machine learning: Strategies and applications.

Neurodegenerative diseases (NDs), characterized by progressive neuronal degeneration and manifesting...

A machine learning model based on placental magnetic resonance imaging and clinical factors to predict fetal growth restriction.

OBJECTIVES: To create a placental radiomics-clinical machine learning model to predict FGR.