Pediatrics

Using machine learning approach to predict suicide ideation and suicide attempts among Chinese adolescents: A cross-sectional study.

BACKGROUND: Screening for suicide ideation and suicide attempts is crucial for adolescents, yet accurately predicting these outcomes remains a significant challenge. The relationship between non-suicidal self-injury and suicide ideation and attempts is complex. Therefore, this study employs machine learning techniques to explore and address these challenges.

DeepSeek's Readiness for Medical Research and Practice: Prospects, Bottlenecks, and Global Regulatory Constraints.

DeepSeek, an open-source multimodal Large Language Model (LLM), was launched by the Chinese startup (Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd.). Despite the lack of advanced artificial intelligence (AI) chips, the performance of its milestone version, "DeepSeek-V3," has set an unprecedented benchmark among LLMs, surpassing existing models. Notably, the opportuni...

Deep learning for fetal inflammatory response diagnosis in the umbilical cord.

INTRODUCTION: Inflammation of the umbilical cord can be seen as a result of ascending intrauterine infection or other inflammatory stimuli. Acute feta...

DeepSeek Deployed in 90 Chinese Tertiary Hospitals: How Artificial Intelligence Is Transforming Clinical Practice.

The integration of artificial intelligence (AI) into clinical practice has reached a new milestone in China, with the deployment of DeepSeek across ne...

Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.

BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the im...

Birth weight prediction using artificial intelligence-based placental assessment from macroscopic photo: a retrospective study.

BACKGROUND: This study aimed to predict newborn birth weight through multifactorial analysis of macroscopic placental images using artificial intellig...

Artificial intelligence in pediatric osteopenia diagnosis: evaluating deep network classification and model interpretability using wrist X-rays.

Osteopenia is a bone disorder that causes low bone density and affects millions of people worldwide. Diagnosis of this condition is commonly achieved ...

Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.

Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool w...

SmartBerry for AI-based growth stage classification and precision nutrition management in strawberry cultivation.

Agriculture is vital for human sustenance and economic stability, with increasing global food demand necessitating innovative practices. Traditional f...

Random forest algorithm for predicting tobacco use and identifying determinants among pregnant women in 26 sub-Saharan African countries: a 2024 analysis.

INTRODUCTION: Tobacco use during pregnancy is a significant public health concern, associated with adverse maternal and neonatal outcomes. Despite its...

Using deep learning for estimation of time-since-injury in pediatric accidental fractures.

BACKGROUND: Estimating time-since-injury of healing fractures is imprecise, encompassing excessively wide timeframes. Most injured children are evalua...

Causal recurrent intervention for cross-modal cardiac image segmentation.

Cross-modal cardiac image segmentation is essential for cardiac disease analysis. In diagnosis, it enables clinicians to obtain more precise informati...

First Clinical Implementation of Step-and-Shoot Proton Arc Therapy for Head and Neck Cancer Treatment.

PURPOSE: Dynamic Spot-scanning Proton Arc (SPArc) therapy has gained attention for enhancing dosimetric plan quality. However, its full clinical imple...

Climate investment and financing, economic growth target constraints and urban green development efficiency.

Climate change constitutes a multidimensional crisis impacting global security and sustainable development, and immediate collective response is neces...

Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors.

We hypothesized that incorporating postnatal dynamic factors would enhance the prediction accuracy of bronchopulmonary dysplasia in preterm infants. T...

Intelligent Care: A Scientometric Analysis of Artificial Intelligence in Precision Medicine.

The integration of advanced computational methods into precision medicine represents a transformative advancement in healthcare, enabling highly perso...

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.

BACKGROUND: Pediatric respiratory diseases, including asthma and pneumonia, are major causes of morbidity and mortality in children. Auscultation of l...

Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.

BACKGROUND: Pediatric asthma is a common chronic respiratory disease worldwide, and its acute exacerbation events significantly impact children's heal...

Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.

Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The ...

Joint resting state and structural networks characterize pediatric bipolar patients compared to healthy controls: a multimodal fusion approach.

Pediatric bipolar disorder (PBD) is a highly debilitating condition, characterized by alternating episodes of mania and depression, with intervening p...