AIMC Topic: Pediatric Obesity

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Multimodal machine learning for analysing multifactorial causes of disease-The case of childhood overweight and obesity in Mexico.

Frontiers in public health
BACKGROUND: Mexico has one of the highest global incidences of paediatric overweight and obesity. Public health interventions have shown only moderate success, possibly from relying on knowledge extracted using limited types of statistical data analy...

Maternal dietary practices during pregnancy and obesity of neonates: a machine learning approach towards hierarchical and nested relationships in a Tibet Plateau cohort study.

The British journal of nutrition
Studies on obesity and risk factors from a life-course perspective among residents in the Tibet Plateau with recent economic growth and increasing obesity are important and urgently needed. The birth cohort in this area provides a unique opportunity ...

Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study.

Artificial intelligence in medicine
Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical s...

Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study.

Endocrine
OBJECTIVE: This study aimed to develop and evaluate machine-learning models for predicting the onset of overweight in adolescents aged 14‒17, utilizing easily collectible personal information.

Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity.

Scientific reports
Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wear...

Prediction of adolescent weight status by machine learning: a population-based study.

BMC public health
BACKGROUND: Adolescent weight problems have become a growing public health concern, making early prediction of non-normal weight status crucial for effective prevention. However, few temporal prediction tools for adolescent four weight status have be...

The association of serum irisin with anthropometric, metabolic, and bone parameters in obese children and adolescents.

Frontiers in endocrinology
BACKGROUND: Irisin is an adipomyokine secreted by muscle and adipose cells, and it plays a role in glucose, fat, and bone metabolism. This study aimed to determine the correlation of serum irisin levels with anthropometric, metabolic, and bone parame...

Automated Deep Learning-Based Segmentation of Abdominal Adipose Tissue on Dixon MRI in Adolescents: A Prospective Population-Based Study.

AJR. American journal of roentgenology
The prevalence of childhood obesity has increased significantly worldwide, highlighting a need for accurate noninvasive quantification of body fat distribution in children. The purpose of this study was to develop and test an automated deep learnin...

Pediatric Single-Incision Robotic Cholecystectomy: A 6-Year Update from a Single Institution.

Journal of laparoendoscopic & advanced surgical techniques. Part A
In children, gallbladder disease has become more common due to the rise in childhood obesity and subsequent shift in etiology. While the gold standard of surgical management remains a laparoscopic technique, there has been increasing interest in rob...

Associations Between Different Dietary Vitamins and the Risk of Obesity in Children and Adolescents: A Machine Learning Approach.

Frontiers in endocrinology
BACKGROUNDS: Simultaneous dietary intake of vitamins is considered as a common and real scenario in daily life. However, limited prospective studies have evaluated the association between multivitamins intake and obesity in children and adolescents.