AIMC Topic: Adolescent

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Representativeness of a German AI-enabled data network for secondary epidemiological analysis based on electronic health records.

PloS one
INTRODUCTION: The ongoing digitalization of medicine, increased computing power and low-cost storage capacities enable the use of AI-based algorithms for epidemiological big data analysis of electronic patient records. The aim of this study was to ev...

Machine learning for screening laryngopharyngeal reflux symptoms in college students: a cross-sectional study.

Annals of medicine
BCKGROUND: Laryngopharyngeal reflux (LPR) is a widespread global health issue. Its recurring symptoms and impact on quality of life create significant economic burdens for individuals and society. To examine the links between lifestyle, diet, and LPR...

Artificial intelligence guidance in ethically challenging clinical scenarios in child and adolescent psychiatry: a qualitative study in the context of Turkiye.

BMC medical ethics
BACKGROUND: Ethical decision-making in child and adolescent psychiatry (CAP) is inherently complex, shaped by developmental vulnerability, evolving autonomy, and competing responsibilities to patients, families, and the legal system. Clinicians often...

Predicting Ultra-High Risk Outcomes Using Linguistic and Acoustic Measures From High-Risk Social Challenge Recordings: mHealth Longitudinal Cohort Exploratory Study.

JMIR formative research
BACKGROUND: Early detection of individuals at ultra-high risk (UHR) for psychosis is critical for timely intervention and improving clinical outcomes. However, current UHR assessments, which rely heavily on psychometric tools, often suffer from low s...

Artificial intelligence for predicting the axial length response of orthokeratology in myopic children.

Physics in medicine and biology
This study aimed to automate the extraction of local corneal topography (CT) features in myopic children undergoing orthokeratology (OK), evaluate their causal effects on axial length (AL) control, and develop a predictive model for AL progression.We...

Diagnostic accuracy of artificial intelligence models in childhood exanthematous diseases: a comparative analysis against clinical diagnosis.

European journal of pediatrics
PURPOSE: Differentiating among exanthematous diseases is frequently challenging due to their overlapping symptomatology. We, therefore, aimed to evaluate the diagnostic accuracy of a consultant physician, a resident physician, and various AI models (...

Ratio maps of T1w/T2w MRI signal intensity do not improve deep-learning segmentation of pediatric brain tumors.

PloS one
INTRODUCTION: T1w/T2w ratio mapping, combining voxel-wise signal intensities in T1-weighted (T1w) and T2-weighted (T2w) structural MRI, has been used to investigate cortical architecture in the brain, but has also shown promise in tissue discriminati...

Detection of cortical arousals in sleep using multimodal wearable sensors and machine learning.

Scientific reports
Cortical arousals are brief brain activations that disrupt sleep continuity and contribute to cardiovascular, cognitive, and behavioral impairments. Although polysomnography is the gold standard for arousal detection, its cost and complexity limit us...

Key personality and training factors influencing athletes' mental health - based on machine learning.

PloS one
Athletes face a higher risk of mental health disorders compared to the general population, and prior theoretical and empirical work suggests that personality traits and training-related factors may play important roles in shaping athletes' mental hea...