AIMC Topic: Child

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Automated Evaluation of D-Score for Facial Dysmorphism Analysis in Central African Children With Developmental Disorders.

Annals of human genetics
INTRODUCTION: Dysmorphism is an important characteristic, but its evaluation is largely subjective. A good clinical assessment (dysmorphism) can facilitate a more accurate and efficient diagnosis. We therefore evaluated an automated artificial intell...

Artificial intelligence in pediatric otolaryngology: A state-of-the-art review of opportunities and pitfalls.

International journal of pediatric otorhinolaryngology
BACKGROUND: Artificial Intelligence (AI) and machine learning (ML) have transformative potential in enhancing diagnostics, treatment planning, and patient management. However, their application in pediatric otolaryngology remains limited as the uniqu...

Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSES: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustness across multiple datasets.

Comparative Analysis of Information Quality in Pediatric Otorhinolaryngology: Clinicians, Residents, and Large Language Models.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Pediatric otorhinolaryngology (ORL) addresses complex conditions in children, requiring a tailored approach for patients and families. With artificial intelligence (AI) gaining traction in medical applications, this study evaluates the qua...

Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation.

Neural networks : the official journal of the International Neural Network Society
Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite its significant successes, this approach has long been facing challenges due to the limited availabi...

Predicting treatment outcome in congenital adrenal hyperplasia using urine steroidomics and machine learning.

European journal of endocrinology
OBJECTIVE: Treatment monitoring of individuals with congenital adrenal hyperplasia (CAH) remains unsatisfactory. Comprehensive 24 h urine steroid profiling provides detailed insight into adrenal steroid pathways. We investigated whether 24 h urine st...

Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis.

BMC public health
Cardiovascular disease (CVD) is a primary cause of death in India, accounting for a significant portion of the global CVD burden. This study looks at statistics on heart disease mortality from the Institute for Health Metrics and Evaluation (IHME) fr...

High-frequency monitoring enables machine learning-based forecasting of acute child malnutrition for early warning.

Proceedings of the National Academy of Sciences of the United States of America
The number of acutely food insecure people worldwide has doubled since 2017, increasing demand for early warning systems (EWS) that can predict food emergencies. Advances in computational methods, and the growing availability of near-real time remote...

Network Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction.

Human brain mapping
Deep learning frameworks utilizing convolutional neural networks (CNNs) have frequently been used for brain age prediction and have achieved outstanding performance. Nevertheless, deep learning remains a black box as it is hard to interpret which bra...