The Role of Artificial Intelligence for Early Diagnostic Tools of Autism Spectrum Disorder: A Systematic Review
Purboyo Solek1, Eka Nurfitri1, Indra Sahril1
1Department of Child Health, Padjadjaran University Faculty of Medicine, Hasan Sadikin General Hospital, West Java, Indonesia.
Insights
Artificial intelligence (AI) shows high accuracy in diagnosing Autism Spectrum Disorder (ASD) in children. AI tools analyze complex data for earlier, more efficient ASD detection and intervention.
Area of Science:
- Neuroscience
- Computer Science
- Pediatrics
Background:
- Autism Spectrum Disorder (ASD) is a neurodevelopmental condition affecting social communication and behavior.
- Accurate and early diagnosis is crucial for effective intervention in pediatric populations.
- Current diagnostic methods can be time-consuming and require specialized expertise.
Purpose of the Study:
- To systematically review the application of artificial intelligence (AI) in diagnosing Autism Spectrum Disorder (ASD).
- To focus on AI's diagnostic capabilities in pediatric populations aged 0-18 years.
- To assess the accuracy and potential of AI in identifying ASD markers.
Main Methods:
- Systematic review adhering to PRISMA 2020 guidelines.
- Searched multiple databases (PubMed, ScienceDirect, etc.) up to November 9, 2024.
- Included studies using AI for ASD diagnosis with a focus on accuracy metrics; excluded non-English and non-diagnostic studies.
- Assessed bias using the Joanna Briggs Institute checklist.
Main Results:
- Included 25 studies utilizing AI for ASD classification across diverse data types (visual, motor, genetic, neuroimaging).
- AI demonstrated high accuracy in identifying ASD, analyzing complex datasets, and detecting subtle markers.
- Studies reported improved diagnostic accuracy, reduced assessment times, and enhanced predictive capabilities.
Conclusions:
- AI integration offers a promising avenue for enhancing ASD diagnostic accuracy, efficiency, and early detection.
- AI can potentially improve accessibility to ASD screening, especially in underserved regions.
- Further research is needed to address data quality, privacy, ethical concerns, and clinical integration challenges for robust AI diagnostic models.
Abstract:
Objective: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social communication and repetitive behaviors. This systematic review examines the application of artificial intelligence (AI) in diagnosing ASD, focusing on pediatric populations aged 0-18 years. Materials and methods: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Inclusion criteria encompassed studies applying AI techniques for ASD diagnosis, primarily evaluated using metriclike accuracy. Non-English articles and studies not focusing on diagnostic applications were excluded. The literature search covered PubMed, ScienceDirect, CENTRAL, ProQuest, Web of Science, and Google Scholar up to November 9, 2024. Bias assessment was performed using the Joanna Briggs Institute checklist for critical appraisal. Results: The review included 25 studies. These studies explored AI-driven approaches that demonstrated high accuracy in classifying ASD using various data modalities, including visual (facial, home videos, eye-tracking), motor function, behavioral, microbiome, genetic, and neuroimaging data. Key findings highlight the efficacy of AI in analyzing complex datasets, identifying subtle ASD markers, and potentially enabling earlier intervention. The studies showed improved diagnostic accuracy, reduced assessment time, and enhanced predictive capabilities. Conclusion: The integration of AI technologies in ASD diagnosis presents a promising frontier for enhancing diagnostic accuracy, efficiency, and early detection. While these tools can increase accessibility to ASD screening in underserved areas, challenges related to data quality, privacy, ethics, and clinical integration remain. Future research should focus on applying diverse AI techniques to large populations for comparative analysis to develop more robust diagnostic models.
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