Colonnese Federica, Di Luzio Francesco, Rosato Antonello, Panella Massimo
Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome "La Sapienza", Via Eudossiana 18, 00184 Rome, Italy.
Sensors (Basel). 2024 Dec 5;24(23):7792. doi: 10.3390/s24237792.
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and repetitive behaviors, often associated with atypical visual attention patterns. In this paper, the Gaze-Based Autism Classifier (GBAC) is proposed, which is a Deep Neural Network model that leverages both data distillation and data attribution techniques to enhance ASD classification accuracy and explainability. Using data sampled by eye tracking sensors, the model identifies unique gaze behaviors linked to ASD and applies an explainability technique called TracIn for data attribution by computing self-influence scores to filter out noisy or anomalous training samples. This refinement process significantly improves both accuracy and computational efficiency, achieving a test accuracy of 94.35% while using only 77% of the dataset, showing that the proposed GBAC outperforms the same model trained on the full dataset and random sample reductions, as well as the benchmarks. Additionally, the data attribution analysis provides insights into the most influential training examples, offering a deeper understanding of how gaze patterns correlate with ASD-specific characteristics. These results underscore the potential of integrating explainable artificial intelligence into neurodevelopmental disorder diagnostics, advancing clinical research by providing deeper insights into the visual attention patterns associated with ASD.
自闭症谱系障碍（ASD）是一种神经发育状况，其特征在于社交沟通和重复行为方面的差异，通常与非典型视觉注意力模式相关。本文提出了基于注视的自闭症分类器（GBAC），它是一种深度神经网络模型，利用数据提炼和数据归因技术来提高ASD分类的准确性和可解释性。该模型使用眼动追踪传感器采样的数据，识别与ASD相关的独特注视行为，并应用一种名为TracIn的可解释性技术进行数据归因，通过计算自我影响分数来过滤掉嘈杂或异常的训练样本。这一细化过程显著提高了准确性和计算效率，仅使用77%的数据集时测试准确率就达到了94.35%，表明所提出的GBAC优于在完整数据集和随机样本缩减情况下训练的相同模型以及基准模型。此外，数据归因分析提供了对最具影响力的训练示例的见解，有助于更深入地理解注视模式与ASD特定特征之间的关联。这些结果强调了将可解释人工智能整合到神经发育障碍诊断中的潜力，通过深入了解与ASD相关的视觉注意力模式来推动临床研究。
