As early intervention is highly effective for young children with autism spectrum disorder (ASD), it is imperative to make accurate diagnosis as early as possible. ASD has often been associated with atypical visual attention and eye gaze data can be collected at a very early age. An automatic screening tool based on eye gaze data that could identify ASD risk offers the opportunity for intervention before the full set of symptoms is present. In this paper, we propose two machine learning methods, synthetic saccade approach and image based approach, to automatically classify ASD given children’s eye gaze data collected from free-viewing tasks of natural images. The first approach uses a generative model of synthetic saccade patterns to represent the baseline scan-path from a typical non-ASD individual and combines it with the real scan-path as well as other auxiliary data as inputs to a deep learning classifier. The second approach adopts a more holistic image-based approach by feeding the input image and a sequence of fixation maps into a convolutional or recurrent neural network. Using a publicly-accessible collection of children’s gaze data, our experiments indicate that the ASD prediction accuracy reaches 67.23% accuracy on the validation dataset and 62.13% accuracy on the test dataset.

由于早期干预对于患有自闭症谱系障碍（ASD）的幼儿非常有效，因此尽早做出准确的诊断势在必行。自闭症谱系障碍通常与非典型的视觉注意力有关，并且可以在很小的时候就收集眼睛注视数据。一种基于眼睛注视数据的自动筛查工具可以识别 ASD 风险，为在出现全套症状之前进行干预提供了机会。在本文中，我们提出了两种机器学习方法，即合成扫视方法和基于图像的方法，根据从自然图像的自由观看任务中收集的儿童眼睛注视数据来自动对 ASD 进行分类。第一种方法使用合成扫视模式的生成模型来表示典型非 ASD 个体的基线扫描路径，并将其与真实扫描路径以及其他辅助数据相结合，作为深度学习分类器的输入。第二种方法采用更全面的基于图像的方法，将输入图像和一系列注视图输入卷积或循环神经网络。使用公开的儿童注视数据集，我们的实验表明，ASD 预测准确率在验证数据集上达到 67.23%，在测试数据集上达到 62.13%。