Multimodal Deep Learning in Early Autism Detection—Recent Advances and Challenges ^†

Autism spectrum disorder (ASD) is a global concern, with a prevalence rate of approximately 1 in 36 children according to estimates from the Centers for Disease Control and Prevention (CDC). Diagnosing ASD poses challenges due to the absence of a definitive medical test. Instead, doctors rely on a comprehensive evaluation of a child’s developmental background and behavior to reach a diagnosis. Although ASD can occasionally be identified in children aged 18 months or younger, a reliable diagnosis by an experienced professional is typically made by the age of two. Early detection of ASD is crucial for timely interventions and improved outcomes. In recent years, the field of early diagnosis of ASD has been greatly impacted by the emergence of deep learning models, which have brought about a revolution by greatly improving the accuracy and efficiency of ASD detection. The objective of this review paper is to examine the recent progress in early ASD detection through the utilization of multimodal deep learning techniques. The analysis revealed that integrating multiple modalities, including neuroimaging, genetics, and behavioral data, is key to achieving higher accuracy in early ASD detection. It is also evident that, while neuroimaging data holds promise and has the potential to contribute to higher accuracy in ASD detection, it is most effective when combined with other modalities. Deep learning models, with their ability to analyze complex patterns and extract meaningful features from large datasets, offer great promise in addressing the challenge of early ASD detection. Among various models used, CNN, DNN, GCN, and hybrid models have exhibited encouraging outcomes in the early detection of ASD. The review highlights the significance of developing accurate and easily accessible tools that utilize artificial intelligence (AI) to aid healthcare professionals, parents, and caregivers in early ASD symptom recognition. These tools would enable timely interventions, ensuring that necessary actions are taken during the initial stages.