Autism spectrum disorder (ASD) is a developmental condition that presents significant challenges in social interaction, communication, and behavior. Early intervention plays a pivotal role in enhancing cognitive abilities and reducing autistic symptoms in children with ASD. Numerous clinical studies have highlighted distinctive facial characteristics that distinguish ASD children from typically developing (TD) children. In this study, we propose a practical solution for ASD screening using facial images using YoloV8 model. By employing YoloV8, a deep learning technique, on a dataset of Kaggle, we achieved exceptional results. Our model achieved a remarkable 89.64% accuracy in classification and an F1-score of 0.89. Our findings provide support for the clinical observations regarding facial feature discrepancies between children with ASD. The high F1-score obtained demonstrates the potential of deep learning models in screening children with ASD. We conclude that the newest version of YoloV8 which is usually used for object detection can be used for classification problem of Austistic and Non-autistic images.

翻译：自闭症谱系障碍（ASD）是一种发育性疾病，对社交互动、沟通和行为构成重大挑战。早期干预在增强ASD儿童认知能力、减轻自闭症症状方面具有关键作用。大量临床研究已指出，ASD儿童与典型发育（TD）儿童存在显著的面部特征差异。本研究提出采用YoloV8模型，基于面部图像实现ASD筛查的实用解决方案。通过在Kaggle数据集上应用YoloV8这一深度学习方法，我们取得了优异成果：模型分类准确率达89.64%，F1分数为0.89。研究结果支持了关于ASD儿童面部特征差异的临床观察。高F1分数证明了深度学习模型在ASD儿童筛查中的潜力。我们得出结论：通常用于目标检测的最新版YoloV8，可有效应用于自闭症与非自闭症图像分类问题。