Pediatrics

Nonlocal models in biology and life sciences: Sources, developments, and applications.

Mathematical modeling is one of the fundamental techniques for understanding biophysical mechanisms in developmental biology. It helps researchers to analyze complex physiological processes and connect like a bridge between theoretical and experimental observations. Various groups of mathematical models have been studied to analyze these processes, and the nonlocal models are one of them. Nonlocal...

Artificial intelligence models predicting abnormal uterine bleeding after COVID-19 vaccination.

The rapid deployment of COVID-19 vaccines has necessitated the ongoing surveillance of adverse events, with abnormal uterine bleeding (AUB) emerging as a reported concern in vaccinated females. We aimed to develop a machine learning (ML) model to predict post-vaccination AUB in women aged less than 50 years. A large-scale national cohort, the Korean Nationwide Cohort (K-COV-N cohort), was utilized...

Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.

Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, i...

Modelling of pome fruit pollen performance using machine learning.

Agriculture, particularly fruit production, is considered a crucial industry with a significant economic impact in many countries. Extreme fluctuation...

The predictive role of sedentary behavior and physical activity on adolescent depressive symptoms: A machine learning approach.

OBJECTIVE: This study aims to investigate the predictive value of sedentary behavior and physical activity in adolescent depressive symptoms.

Continuous non-contact monitoring of neonatal activity.

PURPOSE: Neonatal activity is an important physiological parameter in the neonatal intensive care unit (NICU). The degree of neonatal activity is asso...

Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.

OBJECTIVES: Achieving optimal adherence to antiretroviral therapy (ART) and viral suppression is still insufficient for attaining the UNAIDS 95-95-95 ...

Machine Learning-Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study.

BACKGROUND: To address gaps in global understanding of cultural and social variations, this study used a high-performance machine learning (ML) model ...

Development of the autonomous lab system to support biotechnology research.

In this study, we developed the autonomous lab (ANL), which is a system based on robotics and artificial intelligence (AI) to conduct biotechnology ex...

Enhanced recognition and counting of high-coverage Amorphophallus konjac by integrating UAV RGB imagery and deep learning.

Accurate counting of Amorphophallus konjac (Konjac) plants can offer valuable insights for agricultural management and yield prediction. While current...

Feasibility study of texture-based machine learning approach for early detection of neonatal jaundice.

Untreated neonatal jaundice can have severe consequences. Effective screening for neonatal jaundice can prevent long-term complications in infants. No...

Impact of deep learning on pediatric elbow fracture detection: a systematic review and meta-analysis.

OBJECTIVES: Pediatric elbow fractures are a common injury among children. Recent advancements in artificial intelligence (AI), particularly deep learn...

SegFormer3D: Improving the Robustness of Deep Learning Model-Based Image Segmentation in Ultrasound Volumes of the Pediatric Hip.

Developmental dysplasia of the hip (DDH) is a painful orthopedic malformation diagnosed at birth in 1-3% of all newborns. Left untreated, DDH can lead...

Artificial Intelligence non-invasive methods for neonatal jaundice detection: A review.

Neonatal jaundice is a common and potentially fatal health condition in neonates, especially in low and middle income countries, where it contributes ...

Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching.

Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial ...

Machine learning insights into calcium phosphate nucleation and aggregation.

In this study, we utilized machine learning interatomic potentials (MLIPs) to investigate the nucleation mechanisms of calcium phosphate, a critical c...

AI-facilitated home monitoring for cystic fibrosis exacerbations across pediatric and adult populations.

BACKGROUND: AI-aided home stethoscopes offer the opportunity of continuous remote monitoring of cystic fibrosis (CF) patients, reducing the need for c...

The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas.

Linear and volumetric analysis are the typical methods to measure tumor size. 3D volumetric analysis has risen in popularity; however, this is very ti...

Predicting mother and newborn skin-to-skin contact using a machine learning approach.

BACKGROUND: Despite the known benefits of skin-to-skin contact (SSC), limited data exists on its implementation, especially its influencing factors. T...

Three-dimensional analysis of mandibular and condylar growth using artificial intelligence tools: a comparison of twin-block and Frankel II Appliances.

BACKGROUND: Analyzing the morphological growth changes upon mandibular advancement between Twin Block (TB) and Functional Regulator II (FR2) in Class ...