AIMC Topic: Child

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Plasma proteomics for biomarker discovery in childhood tuberculosis.

Nature communications
Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Current TB diagnostic assays have poor performance in children, thus a global priority is the identificati...

EEG microstate analysis in children with prolonged disorders of consciousness.

Scientific reports
Prolonged disorders of consciousness (pDoC) in children lack objective and effective diagnostic methods to assess consciousness states, hindering targeted treatment selection and delaying recovery. It remains unclear whether EEG microstate analysis, ...

Comparative evaluation of ChatGPT and LLaMA for reliability, quality, and accuracy in familial Mediterranean fever.

European journal of pediatrics
UNLABELLED: Familial Mediterranean fever (FMF) is the most common monogenic autoinflammatory disease. Large language models (LLMs) offer rapid access to medical information. This study evaluated and compared the reliability, quality, and accuracy of ...

An AI-assisted tool for automated growth monitoring in pediatric achondroplasia.

European journal of pediatrics
UNLABELLED: Growth assessment in achondroplasia requires disorder-specific growth charts incorporating sex- and age-specific values. Manual calculations are tedious and subject to error. We present an artificial intelligence (AI)-assisted tool that a...

Ensemble learning for microbiome-based caries diagnosis: multi-group modeling and biological interpretation from salivary and plaque metagenomic data.

BMC oral health
BACKGROUND: Oral microbiota is a major etiological factor in the development of dental caries. Next-generation sequencing techniques have been widely used, generating vast amounts of data which is underexplored. The advancement of artificial intellig...

Artificial Intelligence in Pediatric Anesthesia.

Anesthesiology clinics
This text explores the integration of artificial intelligence (AI) into pediatric anesthesiology, highlighting its potential to enhance safety, efficiency, and decision-making throughout the perioperative period. It addresses the unique challenges of...

Development and evaluation of a deep learning-based system for dental age estimation using the demirjian method on panoramic radiographs.

BMC oral health
BACKGROUND: To develop and evaluate a deep learning-based model for automatic dental age estimation using the Demirjian method on panoramic radiographs, and to compare its performance with the traditional manual approach.

Characterising corneal changes in aniridia-related keratopathy using in vivo confocal microscopy and a self-supervised AI model.

BMJ open ophthalmology
PURPOSE: To investigate whether corneal changes observed via in vivo confocal microscopy (IVCM) in patients with aniridia-related keratopathy (ARK) reflect clinical severity.

Evaluating the Readability of Pediatric Neurocutaneous Syndromes-Related Patient Education Material Created by a Custom GPT With Retrieval Augmentation.

JMIR dermatology
In our study, we developed a GPT assistant with a custom knowledge base for neurocutaneous diseases, tested its ability to answer common patient questions, and showed that a GPT using retrieval augmentation generation can improve the readability of p...

Identification of high-risk hepatoblastoma in the CHIC risk stratification system based on enhanced CT radiomics features.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Survival of patients with high-risk hepatoblastoma remains low, and early identification of high-risk hepatoblastoma is critical.