AIMC Topic: Adolescent

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Predicting the risk of asthma development in youth using machine learning models.

PloS one
Asthma is a chronic respiratory disease characterized by wheezing and difficulty breathing, which disproportionally affects 4.7 million children in the U.S. Currently, there is a lack of asthma predictive models for youth with good performance. This ...

From dry eye to depression: a machine learning-based framework for predicting adolescent mental health.

BMC medical informatics and decision making
BACKGROUND: Adolescent depression is a major public health concern. Physical health indicators are rarely included in risk tools. We examined whether adding dry eye disease (DED) to psychosocial and behavioral factors improves prediction of depressiv...

Integrating machine learning and time-to-event models to explain and predict risk of hospitalization due to dengue in Colombia.

Scientific reports
Arboviral diseases such as dengue pose major public health challenges in endemic regions, notably in Norte de Santander (Colombia), where they place substantial pressure on healthcare services. We analyzed 8,814 confirmed dengue cases reported to the...

Interpretable machine learning for cardiovascular risk prediction: Insights from NHANES dietary and health data.

PloS one
BACKGROUND: Cardiovascular diseases (CVD) are one of the leading global causes of death, which requires an accurate early prediction. This study aimed to develop transparent machine learning (ML) models using National Health and Nutrition Examination...

Cracking the code: a head-to-head comparison of expert clinicians and artificial intelligence in diagnosing rare diseases.

Orphanet journal of rare diseases
BACKGROUND: Patients with rare diseases often face prolonged diagnostic journeys due to the low prevalence and diverse clinical presentations of these conditions. In Germany, specialized centers for rare diseases, established at university hospitals,...

Longitudinal development of sex differences in the limbic system is associated with age, puberty and mental health.

Communications biology
Sex differences in mental health become more evident across adolescence, with a two-fold increase of prevalence of mood disorders in females compared to males. The brain underpinnings remain understudied. Here, we investigated the role of age, pubert...

Learning to Program "Recycles" Preexisting Frontoparietal Population Codes of Logical Algorithms.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Computer programming is a cornerstone of modern society, yet little is known about how the human brain enables this recently invented cultural skill. According to the neural recycling hypothesis, cultural skills (e.g., reading, math) repurpose preexi...

Deep learning-based automated detection of supernumerary teeth in pediatric panoramic radiographs.

PloS one
INTRODUCTION: Supernumerary teeth are a common developmental anomaly in pediatric patients, potentially leading to complications such as impaction, crowding, and delayed eruption. Accurate and early detection is critical to prevent these sequelae and...

Demographic influences on trust in artificial intelligence across cognitive domains: A statistical perspective.

PloS one
As artificial intelligence (AI) systems become increasingly integrated into decision-making across various sectors, understanding public trust in these systems is more crucial than ever. This study presents a quantitative analysis of survey data from...

Quantitative Assessment of Strabismus Using Cloud AI Computing: Validation Study.

JMIR formative research
BACKGROUND: Strabismus measurement is essential in vision assessment and screening. It typically requires skilled clinicians or specialized equipment. Photographic strabismus measurement methods have value in terms of accessibility and convenience of...