Multi-modal data analysis for autism spectrum disorder in children: State of the art and trends

Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder. Early diagnosis and intervention are crucial for improving outcomes. Traditional single-modality diagnostic methods are subjective, limited, and struggle to reveal the underlying pathological mechanisms. In contrast, multimodal data analysis integrates behavioral, physiological, and neuroimaging information with advanced machine-learning and deep-learning algorithms to overcome these limitations. In this review, we surveyed the recent pediatric ASD literature, highlighting artificial intelligence-driven diagnostic techniques, multimodal data fusion strategies, and emerging trends in ASD assessment. We surveyed studies that integrated two or more modalities and summarized the fusion levels, learning paradigms, tasks, datasets, and metrics. Multimodal approaches outperform single-modality baselines in classification, severity estimation, and subtyping by leveraging complementary information and reducing modality-specific biases. Multimodal approaches significantly enhance diagnostic accuracy and comprehensiveness, enabling early screening of ASD, symptom subtyping, severity assessment, and personalized interventions. Advances in multimodal fusion techniques have promoted progress in precision medicine for the treatment of ASD. • Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder. • Traditional diagnosis is subjective and fails to uncover pathological mechanisms. • Artificial intelligence-driven multimodal data fusion is a new ASD assessment option. • Multimodal approaches enhance diagnostic accuracy and enable early ASD screening.