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Learning Disabilities

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...

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Digitally Diagnosing Multiple Developmental Delays using Crowdsourcing fused with Machine Learning: A Research

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Summary

This study introduces a gamified system using AI and crowdsourcing to improve early diagnosis of Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) in children, addressing accessibility barriers.

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Area of Science:

Digital health
Artificial Intelligence in Psychiatry
Child Psychology

Background:

Approximately 17% of US minors (3-17 years) have developmental or psychiatric conditions, with underdiagnosis in rural and minority populations.
Limited access to timely diagnostic services due to cost, distance, and clinician availability hinders early intervention.
Digital phenotyping offers potential for accessible evaluations, reducing time-to-diagnosis for pediatric psychiatric conditions.

Approach:

Development of a gamified web system for adaptive video data collection of social interactions.
Integration of novel crowdsourcing algorithms with machine learning for behavioral feature extraction.
Creation of machine learning models for simultaneous diagnosis of Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD), with adaptive data acquisition.

Key Points:

Novel approach combines gamification, crowdsourcing, and machine learning for behavioral analysis.
Focus on simultaneous prediction of ASD and ADHD, conditions with overlapping social behaviors.
Adaptive ML models enhance diagnostic precision by requesting further information when uncertain.

Conclusions:

The proposed AI-powered tool has the potential to be the first to accurately distinguish between ASD and ADHD based on complex social behaviors.
This system could significantly improve diagnostic accessibility and timeliness for pediatric developmental and psychiatric conditions.
Advancing AI in digital phenotyping can bridge gaps in current diagnostic services, especially for underserved populations.