AIMC Topic: Adolescent

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Real-life benefit of artificial intelligence-based fracture detection in a pediatric emergency department.

European radiology
OBJECTIVES: This study aimed to evaluate the performance of an artificial intelligence (AI)-based software for fracture detection in pediatric patients within a real-life clinical setting. Specifically, it sought to assess (1) the stand-alone AI perf...

Skull CT metadata for automatic bone age assessment by using three-dimensional deep learning framework.

International journal of legal medicine
Bone age assessment (BAA) means challenging tasks in forensic science especially in some extreme situations like only skulls found. This study aimed to develop an accurate three-dimensional deep learning (DL) framework at skull CT metadata for BAA an...

A vaccine chatbot intervention for parents to improve HPV vaccination uptake among middle school girls: a cluster randomized trial.

Nature medicine
Conversational artificial intelligence, in the form of chatbots powered by large language models, offers a new approach to facilitating human-like interactions, yet its efficacy in enhancing vaccination uptake remains under-investigated. This study a...

Prediction of moderate to severe bleeding risk in pediatric immune thrombocytopenia using machine learning.

European journal of pediatrics
UNLABELLED: This study aimed to develop and validate a risk prediction model for moderate to severe bleeding in children with immune thrombocytopenia (ITP). Data from 286 ITP patients were prospectively collected and randomly split into training (80%...

Evaluation of a deep learning segmentation tool to help detect spinal cord lesions from combined T2 and STIR acquisitions in people with multiple sclerosis.

European radiology
OBJECTIVE: To develop a deep learning (DL) model for the detection of spinal cord (SC) multiple sclerosis (MS) lesions from both sagittal T2 and short tau inversion recovery (STIR) sequences and to investigate whether such a model could improve the p...

Deep learning model for detecting cystoid fluid collections on optical coherence tomography in X-linked retinoschisis patients.

Acta ophthalmologica
PURPOSE: To validate a deep learning (DL) framework for detecting and quantifying cystoid fluid collections (CFC) on spectral-domain optical coherence tomography (SD-OCT) in X-linked retinoschisis (XLRS) patients.

Transforming physical fitness and exercise behaviors in adolescent health using a life log sharing model.

Frontiers in public health
INTRODUCTION: This study investigates the potential of a deep learning-based Life Log Sharing Model (LLSM) to enhance adolescent physical fitness and exercise behaviors through personalized public health interventions.

Machine learning approach for dosage individualization of azithromycin in children with community-acquired pneumonia.

British journal of clinical pharmacology
AIMS: The uncertainty about the efficacy and safety of currently used azithromycin dosing regimens in children warrants individualized therapy. The area under the plasma concentration-time curve over 24 h (AUC) of azithromycin correlates best with it...

Early obesity risk prediction via non-dietary lifestyle factors using machine learning approaches.

Clinical obesity
Obesity poses a significant health threat, contributing to the development of noncommunicable diseases (NCDs). Early identification of individuals at higher risk for obesity is crucial for implementing effective prevention strategies. This study expl...

A machine learning model to predict intradialytic hypotension in pediatric continuous kidney replacement therapy.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Intradialytic hypotension (IDH) is associated with mortality in adults undergoing intermittent hemodialysis, but this relationship is unclear in critically ill children receiving continuous kidney replacement therapy (CKRT). We aim to eva...