Participant characteristics.

Background
Children with high autistic traits often exhibit deficits in drawing, an important skill for social adaptability. Machine learning is a powerful technique for learning predictive models from movement data, so drawing processes and product characteristics can be objectively evaluated. This study aimed to assess the potential of evaluating shape drawing using machine learning to predict high autistic traits.
Method
Seventy boys (5.03 ± 0.16) and 63 girls (5.06 ± 0.18) from the general population participated in the study. Participants were asked to draw shapes in the following order: equilateral triangle, inverted equilateral triangle, square, and the sun. A model for classifying participants as likely to have high autistic traits was developed using a support vector machine algorithm with a linear kernel utilizing 16 variables. A 16-inch liquid crystal display pen tablet was used to acquire data on hand-finger fine motor activity while the participants drew each shape. The X and Y coordinates of the pen tip, pen pressure, pen orientation, pen tilt, and eye movements were recorded to determine whether the participants had any problems with this skill. Eye movements were assessed using a webcam. These data and eye movements were used to identify the variables for the support vector machine model.
Data and Results
For each shape, a model support vector machine was created to classify the high and low autistic trait groups, with accuracy, sensitivity, and specificity all above 85%. The specificity values across all models were 100%. In the inverted equilateral triangle model, specificity, accuracy, and sensitivity values were 100%.
Conclusions
These results demonstrate the potential of assessing shape characteristics using machine learning to predict high levels of autistic traits. Future studies with a wider variety of shapes are warranted to establish further the potential efficacy of drawing skills for screening for autism spectrum conditions.

研究背景
具有较高自闭症特质的儿童往往在绘画能力上存在缺陷，而绘画是影响社会适应能力的一项重要技能。机器学习是一种可从运动数据中学习预测模型的强大技术，能够对绘画过程与作品特征进行客观评估。本研究旨在评估利用机器学习分析形状绘画、以预测高自闭症特质的潜力。

研究方法
本研究纳入了来自普通人群的70名男孩（年龄均值±标准差：5.03±0.16岁）与63名女孩（5.06±0.18岁）。参与者需按照以下顺序绘制图形：等边三角形、倒等边三角形、正方形与太阳。本研究采用带有线性核的支持向量机（Support Vector Machine, SVM）算法，基于16个变量构建分类模型，以区分高自闭症特质与非高自闭症特质人群。研究使用16英寸液晶数位板采集参与者绘制各图形时的手-指精细运动活动数据，记录笔尖X、Y坐标、笔压、笔方位、笔倾斜度以及眼动数据，以评估参与者在该项技能上是否存在障碍；眼动数据通过网络摄像头采集。上述数据与眼动数据被用于提取支持向量机模型所需的特征变量。

数据与结果
针对每种绘制图形，本研究均构建了支持向量机分类模型，以区分高、低自闭症特质组，模型的准确率、灵敏度与特异度均高于85%。所有模型的特异度均为100%；其中倒等边三角形分类模型的特异度、准确率与灵敏度均达到100%。

研究结论
上述结果证实了利用机器学习分析绘画形状特征以预测高自闭症特质的潜力。未来可纳入更多种类的图形开展研究，以进一步验证绘画技能在自闭症谱系障碍筛查中的应用价值。