A comprehensive study on detection of autism signs through visual attention features and machine learning models

Autism spectrum disorder (ASD) is characterized by difficulties in social interaction, verbal & nonverbal communication, and restricted & repetitive behaviors. Early detection of ASD is crucial for timely intervention, yet it remains a challenging task due to the heterogeneous nature of its manifestations. Growing evidence indicates that existing screening methods, such as the Modified Checklist for Autism in Toddlers, may lack adequate precision for the early identification of children with ASD in clinical settings. Eye tracking captures subtle differences in visual attention patterns, including fixation duration, saccadic movements, and gaze direction, which are known to differ between autistic and typically developing children. The prime focus of the proposed study is to provide a comprehensive summary on the detection of autism signs by applying machine learning algorithms on eye tracking data. The eye features that hold discriminative data for the classification of autistic and typically developing children are explored in the study. The state-of-the-art methods for the detection of autism signs through eye features are presented in a coherent manner. The results of the existing methods on the classification of autistic and typically developing children are discussed in detail. The study addresses significant issues in this field, including data scarcity, class imbalance, and the interpretability of machine learning models, while also emphasizing solutions such as synthetic data production, feature selection methods, and hybrid model architectures. This review emphasizes the transformative potential of integrating eye tracking technology with machine learning to advance ASD diagnostics by synthesizing existing literature.