AIMC Topic: Child

Clear Filters Showing 2831 to 2840 of 3433 articles

Evaluating efficacy of 0.125% atropine using a myopia progression machine learning model.

Japanese journal of ophthalmology
PURPOSE: To investigate the usefulness of a machine learning (ML) model that can predict the natural course of childhood myopia in evaluation of the inhibitory effects of 0.125% atropine on the progression of childhood myopia.

Machine-Learning Assisted Screening with FIND FH for Familial Hypercholesterolemia among Youth.

The Journal of pediatrics
Although the American Academy of Pediatrics recommends universal lipid screening among children to find cases of familial hypercholesterolemia, such screening is rarely performed. We report the first clinical use of a novel machine learning model (FI...

A robot's efficient demonstration cannot reduce 5- to 6-year-old children's over-imitation.

Journal of experimental child psychology
Children tend to imitate inefficient behaviors containing causally irrelevant actions-they over-imitate. Out-group members' efficient demonstration cannot reduce children's over-imitation of in-group members, due to their interpretation of irrelevant...

Population-Driven Synthesis of Personalized Cranial Development From Cross-Sectional Pediatric CT Images.

IEEE transactions on bio-medical engineering
OBJECTIVE: Predicting normative pediatric growth is crucial to identify developmental anomalies. While traditional statistical and computational methods have shown promising results predicting personalized development, they either rely on statistical...

Reliability and validity of artificial intelligence-based innovative digital scale for the assessment of anxiety in children.

European journal of paediatric dentistry
AIM: To assess the reliability and validity of an AI-based, innovative digital scale for the assessment of dental anxiety in children.

Machine learning methods for sex estimation of sub-adults using cranial computed tomography images.

Anthropologischer Anzeiger; Bericht uber die biologisch-anthropologische Literatur
This research aimed to compare the classification accuracy of three machine learning (ML) methods (random forest (RF), support vector machines (SVM), linear discriminant analysis (LDA)) for sex estimation of sub-adults using cranial computed tomograp...

Algorithmic Fairness in Machine Learning Prediction of Autism Using Electronic Health Records.

Studies in health technology and informatics
Efforts to improve early diagnosis of autism spectrum disorder (ASD) in children are beginning to use machine learning (ML) approaches applied to real-world clinical datasets, such as electronic health records (EHRs). However, sex-based disparities i...

Exploring Machine Learning for Predicting Peripheral and Central Precocious Puberty Through Cross-Hospital Validation.

Studies in health technology and informatics
Precocious puberty, including Peripheral Precocious Puberty (PPP) and Central Precocious Puberty (CPP), presents diagnostic challenges in pediatric endocrinology, leading to delayed interventions. This study utilized machine learning models-Random Fo...

Diagnostic Machine Learning Models of Infectious Mononucleosis in Children Based on Clinical Data: A Retrospective Multicenter Study.

Journal of medical virology
The clinical manifestations of infectious mononucleosis (IM) and acute respiratory tract infections (ARTI) exhibit significant similarities. We aim to develop cost-efficient models for IM in children utilizing the Shapley Additive explanation (SHAP) ...

Prediction Model for Insulin Resistance and Implications for MASLD in Youth: A Novel Marker, the Pediatric Insulin Resistance Assessment Score.

Yonsei medical journal
PURPOSE: Insulin resistance (IR) is a condition closely associated with cardiovascular risk factors and metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a significant IR-related complication. We aimed to develop a predi...