Abstract
Early detection of autism, a neurodevelopmental disorder marked by social communication challenges, is crucial for timely intervention. Recent advancements have utilized naturalistic home videos captured via the mobile application GuessWhat. Through interactive games played between children and their guardians, GuessWhat has amassed over 3,000 structured videos from 382 children, both diagnosed with and without Autism Spectrum Disorder (ASD). This collection provides a robust dataset for training computer vision models to detect ASD-related phenotypic markers, including variations in emotional expression, eye contact, and head movements. We have developed a protocol to curate high-quality videos from this dataset, forming a comprehensive training set. Utilizing this set, we trained individual LSTM-based models using eye gaze, head positions, and facial landmarks as input features, achieving test AUCs of 86%, 67%, and 78%, respectively. To boost diagnostic accuracy, we applied late fusion techniques to create ensemble models, improving the overall AUC to 90%. This approach also yielded more equitable results across different genders and age groups. Our methodology offers a significant step forward in the early detection of ASD by potentially reducing the reliance on subjective assessments and making early identification more accessibly and equitable.
Abstract (translated)
早期对自闭症(ASD)的检测,这是一种以社交沟通困难为特征的神经发育障碍,对及时干预至关重要。最近的研究利用了通过移动应用程序GuessWhat捕捉的自然家庭视频,通过儿童和监护人之间的互动游戏累积了超过3,000个结构化视频,这些视频来自被确诊为ASD和未确诊ASD的382名儿童。这个数据集为训练计算机视觉模型检测ASD相关表型提供了有力的数据,包括情感表达、眼神接触和头部运动的变异性。我们开发了一个从该数据集中筛选高质量视频的协议,形成了一个全面的训练集。利用这个集,我们使用眼部注视、头部位置和面部关键点作为输入特征训练了基于LSTM的个体模型,实现了86%、67%和78%的测试AUC。为了提高诊断准确性,我们应用了晚融合技术创建了集成模型,提高了整体AUC至90%。这种方法还产生了更平等的结果,跨越了不同性别和年龄组。我们的方法在通过可能减少对主观评估的依赖来显著提高ASD早期诊断方面迈出了重要的一步,使早期识别更加容易和公平。
URL
https://arxiv.org/abs/2408.13255
