Machine Learning Based Autism Spectrum Disorder Detection from Videos.
Machine Learning Based Autism Spectrum Disorder Detection from Videos.
复制标题
基于机器学习的自闭症谱系障碍从视频中检测。
DOI：
10.1109/healthcom49281.2021.9398924
复制
发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Young G
中科院分区：
文献类型：
--
作者：
Wu C;Liaqat S;Helvaci H;Cheung SS;Chuah CN;Ozonoff S;Young G
自闭症谱系障碍（ASD）的早期诊断对于最佳结果至关重要。在本文中，我们提出了一种基于确定6至36个月婴儿视频的特定行为的机器学习（ML）方法来诊断ASD诊断。感兴趣的行为包括针对感兴趣的面孔或对象，积极影响和发声。数据集由2000个3分钟持续时间的视频组成，并由专家评估者手动编码这些行为。此外，数据集具有统计特征，包括视频收集中上述行为的持续时间和频率以及临床医生独立的ASD诊断。我们以两阶段的方法解决了ML问题。首先，我们开发了深度学习模型，以自动识别婴儿在与父母或专家临床医生的一对一互动中表现出的临床相关行为。我们使用两种方法报告行为分类的基线结果：（1）基于图像的模型（2）基于面部行为特征的模型。我们达到70％的微笑精度，外观面部的精度为68％，外观对象的67％和发声精度为53％。其次，我们通过应用特征选择过程来确定最重要的统计行为特征以及在抽样过程中以减轻类不平衡，然后开发基线ML分类器，以实现ASD诊断的精度为82％，我们将重点放在ASD诊断预测上。
Early diagnosis of Autism Spectrum Disorder (ASD) is crucial for best outcomes to interventions. In this paper, we present a machine learning (ML) approach to ASD diagnosis based on identifying specific behaviors from videos of infants of ages 6 through 36 months. The behaviors of interest include directed gaze towards faces or objects of interest, positive affect, and vocalization. The dataset consists of 2000 videos of 3-minute duration with these behaviors manually coded by expert raters. Moreover, the dataset has statistical features including duration and frequency of the above mentioned behaviors in the video collection as well as independent ASD diagnosis by clinicians. We tackle the ML problem in a two-stage approach. Firstly, we develop deep learning models for automatic identification of clinically relevant behaviors exhibited by infants in a one-on-one interaction setting with parents or expert clinicians. We report baseline results of behavior classification using two methods: (1) image based model (2) facial behavior features based model. We achieve 70% accuracy for smile, 68% accuracy for look face, 67% for look object and 53% accuracy for vocalization. Secondly, we focus on ASD diagnosis prediction by applying a feature selection process to identify the most significant statistical behavioral features and a over and under sampling process to mitigate the class imbalance, followed by developing a baseline ML classifier to achieve an accuracy of 82% for ASD diagnosis.