Graph Neural Networks and sequential architectures for autism detection from eye-tracking biomarkers: a multi-site study.

backgroundAutism Spectrum Disorder (ASD) affects 1 in 100 children globally, yet early detection remains challenging due to reliance on subjective behavioral assessments and limited specialist availability, particularly in resource-constrained settings. Eye-tracking biomarkers offer objective, quantitative measurements of visual attention patterns, but existing Neural Network approaches lack systematic evaluation across diverse neural network architectures and multi-site data. This study provides a comprehensive comparison of Graph Neural Networks, Long Short-Term Memory networks, and Multilayer Perceptrons for ASD detection using multimodal eye-tracking features.

methodsWe combined two independent datasets: CILIA (57 participants from France, structured viewing paradigm) and Saliency4ASD (27 participants from Italy, free-viewing paradigm), creating a diverse multi-site European cohort of 84 participants (40 ASD, 44 typically developing). Four neural network architectures were evaluated: Graph Neural Networks (GNNs) with attention mechanisms, GNN without attention, bidirectional Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) networks. Eye-tracking features included fixation patterns, saccade dynamics, pupil diameter, and spatial attention distributions. Leave-one-subject-out cross-validation ensured robust generalization. Statistical comparisons employed DeLong tests for area under the ROC curve differences, McNemar tests for accuracy, and bootstrap confidence intervals (10,000 iterations). Per-dataset performance analysis assessed generalization across acquisition protocols.

resultsLSTM achieved the highest AUC of 0.859 (95% CI: 0.770-0.934), followed by GNN with attention (AUC = 0.855, 95% CI: 0.763-0.931). Contrary to expectations, attention mechanisms did not significantly improve GNN performance (GNN without attention: AUC = 0.823, p = 0.102). LSTM significantly outperformed MLP (AUC = 0.811, p = 0.002) but showed no significant difference versus GNN with attention (p = 0.749). GNN with attention demonstrated the highest specificity (93.2%) and positive predictive value (90.6%). Performance remained consistent across both datasets (LSTM: AUC = 0.840 on CILIA versus 0.956 on Saliency4ASD), supporting generalization across datasets despite different viewing paradigms, equipment, and cultural populations. All models exhibited large effect sizes (Cohen's d exceeding 1.0) for discriminating ASD from typically developing predictions.

conclusionsThis systematic evaluation demonstrates that LSTM and graph-based approaches achieve equivalent performance for ASD detection from eye-tracking biomarkers, with attention mechanisms providing no significant benefit. The high specificity (93.2%), real-time capability, and cross-dataset generalization make these models viable for scalable, objective ASD screening in clinical settings, potentially enabling earlier intervention in resource-constrained environments.