Early Detection and Diagnosis of Autism Spectrum Disorder: Challenges and Innovations
DOI:
https://doi.org/10.53573/rhimrj.2024.v11n7.005Keywords:
Cryptocurrency, Investor Sentiments, Market Penetration, India, Regulatory ChallengesAbstract
The early detection and diagnosis of Autism Spectrum Disorder (ASD) is critical for improving long-term developmental outcomes through timely interventions. This review explores evolving methodologies aimed at identifying ASD in its earliest stages, including genetic screenings, behavioural assessments, and advanced diagnostic models based on machine learning. Genetic screenings offer insights into potential biomarkers, while behavioural assessments remain a cornerstone for clinical evaluation. Additionally, innovations in machine learning algorithms are showing promise in analyzing large datasets to predict ASD-related patterns. However, significant challenges persist, including differentiating ASD from other neurodevelopmental disorders with overlapping symptoms, variability in symptom presentation across individuals, and the lack of universally accepted diagnostic criteria for younger age groups. This review also addresses practical barriers such as access to specialized healthcare, the need for cultural adaptability of assessment tools, and the role of parent-reported assessments. Understanding these challenges and innovations will aid researchers, clinicians, and policymakers in refining early diagnostic frameworks, with the ultimate goal of facilitating early and accurate interventions.
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