AIMC Topic: Child

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Short Research Article: Evaluation of an artificial intelligence language model in psychiatric patient education.

Child and adolescent mental health
BACKGROUND: The incorporation of artificial intelligence (AI) and machine learning (ML) into medicine has enhanced clinical information processing. ChatGPT, an AI language model, has demonstrated proficiency in generating human-like responses to comp...

A multidimensional prediction model for overtraining risk in youth soccer players: Integrating physiological and psychological markers.

Journal of sports sciences
Overtraining syndrome (OTS) poses a critical challenge in youth soccer, particularly during periods of rapid physiological maturation combined with high training demands. This study aimed to develop and validate a multidimensional prediction model fo...

Dental caries detection in children using intraoral scans and deep learning.

Journal of dentistry
OBJECTIVE: This study aimed to demonstrate the use of deep learning for automating caries detection using intraoral scan data from children and to evaluate diagnostic agreement between the models' predictions and dental practitioner assessments on 3D...

Temporal convolutional neural network-based feature extraction and asynchronous channel information fusion method for heart abnormality detection in phonocardiograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Auscultation-based cardiac abnormality detection is valuable screening approach in pediatric populations, particularly in resource-limited settings. However, its clinical utility is often limited by phonocardiogram (PCG) sig...

Classification of epilepsy seizure types in pediatrics based on Turkish EEG reports.

Epilepsy research
This study focuses on the binary classification of pediatric epilepsy seizure types as focal or generalized using Turkish electroencephalography (EEG) reports, leveraging natural language processing (NLP) and machine learning methodologies. A novel d...

A Mixed-attention Network for Automated Interventricular Septum Segmentation in Bright-blood Myocardial T2* MRI Relaxometry in Thalassemia.

Academic radiology
RATIONALE AND OBJECTIVES: This study develops a deep-learning method for automatic segmentation of the interventricular septum (IS) in MR images to measure myocardial T2* and estimate cardiac iron deposition in patients with thalassemia.

SFPGCL: Specificity-preserving federated population graph contrastive learning for multi-site ASD identification using rs-fMRI data.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that affects people's social communication and daily routine. Most existing imaging studies on ASD use single site resting-state functional magnetic resonance imaging (rs-fMRI) da...

An X-ray bone age assessment method for hands and wrists of adolescents in Western China based on feature fusion deep learning models.

International journal of legal medicine
The epiphyses of the hand and wrist serve as crucial indicators for assessing skeletal maturity in adolescents. This study aimed to develop a deep learning (DL) model for bone age (BA) assessment using hand and wrist X-ray images, addressing the chal...

Multimodal MRI radiomics enhances epilepsy prediction in pediatric low-grade glioma patients.

Journal of neuro-oncology
BACKGROUND: Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperative evaluation. Therefore, we aim to propose an innovative, machine learning- and deep learning-based ...