AIMC Topic: Child

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Deep Learning Algorithms to Predict Differential Renal Function <40% in Unilateral Hydronephrosis Based on Key Parameters of Urinary Tract Ultrasound.

Urology
OBJECTIVE: To identify the correlation between ultrasound findings and the incidence of differential renal function (DRF) <40%, we conducted an analysis of the key parameters of urinary tract ultrasound in children with unilateral hydronephrosis. For...

Comparative analysis of GPT-4 and Google Gemini's consistency with pediatric otolaryngology guidelines.

International journal of pediatric otorhinolaryngology
OBJECTIVE: To evaluate the accuracy and completeness of large language models (LLMs) in interpreting pediatric otolaryngology guidelines.

Comparison of deep learning models for facial attractiveness assessment on 3D photos.

Journal of dentistry
OBJECTIVES: Convolutional neural networks (CNNs) have demonstrated remarkable success in orthodontics. This study aimed to evaluate the accuracy and precision of several prominent CNN models for evaluating the facial attractiveness in Chinese orthodo...

Explainable transformer-based deep survival analysis in childhood acute lymphoblastic leukemia.

Computers in biology and medicine
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common type of leukemia among children and adolescents and can be life-threatening. The incidence of new cases has been increasing in recent years. Developing a predictive model to forecast t...

Data independent acquisition proteomics and machine learning reveals that proteins associated with immunity are potential molecular markers for early diagnosis of autism.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Early diagnosis of autism is critical to its treatment, but so far, there is no clear molecular marker for early diagnosis in children.

Generating Synthetic T2*-Weighted Gradient Echo Images of the Knee with an Open-source Deep Learning Model.

Academic radiology
RATIONALE AND OBJECTIVES: Routine knee MRI protocols for 1.5 T and 3 T scanners, do not include T2*-w gradient echo (T2*W) images, which are useful in several clinical scenarios such as the assessment of cartilage, synovial blooming (deposition of he...

A Hierarchical Graph Convolutional Network With Infomax-Guided Graph Embedding for Population-Based ASD Detection.

IEEE journal of biomedical and health informatics
Recently, functional magnetic resonance imaging (fMRI)-based brain networks have been shown to be an effective diagnostic tool with great potential for accurately detecting autism spectrum disorders (ASD). Meanwhile, the successful use of graph convo...

Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.

Pediatric cardiology
Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artificial intelligence-enhanced ECG analysis shows promise to detect ASD2 in adults. However, its applicat...

Development and validation of an integrated residual-recurrent neural network model for automated heart murmur detection in pediatric populations.

Scientific reports
Congenital heart disease affects approximately 1% of children worldwide, with a number of cases in resource-limited settings remaining undiagnosed through school age. While cardiac auscultation is a key screening method, its effectiveness varies wide...

AI for chronic pain in children: a powerful resource.

BMC pediatrics
Given the lack of scientific evidence, chronic pain represents an arduous challenge, especially in the pediatric field. In this complex scenario, artificial intelligence (AI) could support diagnosis, therapy, and research. However, the great potentia...