Decoding Autism: A Comprehensive Review of Adolescent Symptom Measurement Scales
DOI:
https://doi.org/10.52783/jns.v14.2007Keywords:
Autism Spectrum Disorder, adolescent ASD diagnosis, psychometric evaluation of autism scales, AI-driven autism diagnostics, culturally adapted autism toolsAbstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that presents significant challenges in social communication, behavior, and cognitive functioning, particularly during adolescence. Accurate assessment of ASD symptoms in adolescents is crucial for timely intervention, improved quality of life, and effective research outcomes. This paper provides a comprehensive review of the autism measurement scales used in adolescent populations, examining their psychometric properties, clinical applicability, and emerging diagnostic tools. Traditional assessment methods, including the Autism Diagnostic Observation Schedule (ADOS), Childhood Autism Rating Scale (CARS), Social Responsiveness Scale (SRS), Autism Spectrum Quotient (AQ), and Developmental, Dimensional, and Diagnostic Interview (3Di) have been evaluated for their strengths and limitations. Additionally, this study explores emerging AI-driven assessment tools and culturally adapted scales used in India. This study employed a systematic literature review methodology, analyzing24 peer-reviewed research papers published between 2000 and 2024. Key findings indicate that the ADOS and CARS remain the most reliable diagnostic instruments, while SRS and AQ serve as effective screening tools. However, subjective biases, time constraints, and accessibility issues pose challenges to their widespread adoption. This study highlights the importance of integrating digital diagnostic platforms, wearable biosensors, and machine learning algorithms to enhance the accuracy and efficiency of autism assessments. By synthesizing the current research, this study aimed to assist clinicians, educators, and researchers in selecting the most appropriate diagnostic tools for adolescents with ASD, ultimately contributing to improved diagnostic precision and targeted intervention strategies.
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