AIMC Topic: Adolescent

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Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany.

Scientific reports
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resou...

Commentary to "Translational machine learning for child and adolescent psychiatry".

Journal of child psychology and psychiatry, and allied disciplines
In this commentary on 'Translational Machine Learning for Child and Adolescent Psychiatry,' by Dwyer and Koutsouleris, we summarize some of the main points made by the authors, which highlight the importance of emerging applications of machine learni...

Artificial intelligence in computed tomography for quantifying lung changes in the era of CFTR modulators.

The European respiratory journal
BACKGROUND: Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease . However, visual scoring systems as an outcome measure are time consuming, require training and lack high reprod...

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

AJR. American journal of roentgenology
Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral level, and skeletal muscle index (SMI), representing height-normalized SMA, can serve as markers of sarcopenia. Normal SMA and SMI values have been rep...

Pediatric age estimation from radiographs of the knee using deep learning.

European radiology
OBJECTIVES: Age estimation, especially in pediatric patients, is regularly used in different contexts ranging from forensic over medicolegal to clinical applications. A deep neural network has been developed to automatically estimate chronological ag...

Construction of artificial intelligence system of carpal bone age for Chinese children based on China-05 standard.

Medical physics
PURPOSE: The purpose of this study is to construct an automatic carpal bone age evaluation system for Chinese children based on TW3-C Carpal method by deep learning and to evaluate the accuracies in test set and clinical test set.

Predicting age at onset of type 1 diabetes in children using regression, artificial neural network and Random Forest: A case study in Saudi Arabia.

PloS one
The rising incidence of type 1 diabetes (T1D) among children is an increasing concern globally. A reliable estimate of the age at onset of T1D in children would facilitate intervention plans for medical practitioners to reduce the problems with delay...

Technology Matters: Machine learning approaches to personalised child and adolescent mental health care.

Child and adolescent mental health
There has been much interest in the potential for machine learning and artificial intelligence to enhance health care. In this article, we discuss the potential applications of the technology to child and adolescent mental health services (CAMHS). We...

Machine Learning-Based MRI LAVA Dynamic Enhanced Scanning for the Diagnosis of Hilar Lesions.

Computational and mathematical methods in medicine
OBJECTIVE: To explore the value of machine learning-based magnetic resonance imaging (MRI) liver acceleration volume acquisition (LAVA) dynamic enhanced scanning for diagnosing hilar lesions.

Perceptions and Profiles of Young People Regarding Spa Tourism: A Comparative Study of Students from Granada and Aachen Universities.

International journal of environmental research and public health
Spa tourism has undergone important changes in recent decades, actively embracing wellness and wellbeing. However, this transition is taking place in different ways in Europe, and this has led to varying perceptions of thermalism that have little to ...