Su Wan-Chun, Mutersbaugh John, Huang Wei-Lun, Bhat Anjana, Gandjbakhche Amir
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health, Building 49, Room 5A82, 49 Convent Drive, Bethesda, MD, 20892-4480, USA.
School of Kinesiology, Louisiana State University, Baton Rouge, LA, USA.
Sci Rep. 2024 Dec 5;14(1):30283. doi: 10.1038/s41598-024-81652-z.
Autism Spectrum Disorder (ASD) is among the most prevalent neurodevelopmental disorders, yet the current diagnostic procedures rely on behavioral analyses and interviews, without objective screening methods to support the diagnostic process. This study seeks to address this gap by integrating upper limb kinematics and deep learning methods to identify potential biomarkers that could be validated in younger age groups in the future to enhance the identification of ASD. Forty-one school-age children, with and without an ASD diagnosis (mean age ± SE: TD group: 10.3 ± 0.8, 8 males and 7 females; ASD group: 10.3 ± 0.5, 21 males and 5 females), participated in the study. A single Inertial Measurement Unit (IMU) was affixed to the child's wrist as they engaged in a continuous reaching and placing task. Deep learning techniques were employed to classify children with and without ASD. Our findings suggest differential movement kinematics in school-age children compared to healthy adults. Compared to TD children, children with ASD exhibited poor feedforward/feedback control of arm movements as seen by greater number of movement units, more movement overshooting, and prolonged time to peak velocity/acceleration. Unique movement strategies such as greater velocity and acceleration were also seen in the ASD group. More importantly, using Multilayer Perceptron (MLP) model, we demonstrated an accuracy of ~ 78.1% in classifying children with and without ASD. These findings underscore the potential use of studying upper limb movement kinematics during goal-directed arm movements and deep learning methods as valuable tools for classifying and, consequently, aiding in the diagnosis and early identification of ASD upon further validation of their specificity among younger children.
自闭症谱系障碍（ASD）是最常见的神经发育障碍之一，但目前的诊断程序依赖于行为分析和访谈，缺乏客观的筛查方法来支持诊断过程。本研究旨在通过整合上肢运动学和深度学习方法来解决这一差距，以识别潜在的生物标志物，这些标志物未来可在更年幼的年龄组中得到验证，以加强对ASD的识别。41名学龄儿童参与了该研究，其中有和没有ASD诊断（平均年龄±标准误：TD组：10.3±0.8，8名男性和7名女性；ASD组：10.3±0.5，21名男性和5名女性）。当孩子进行连续的伸手和放置任务时，将一个惯性测量单元（IMU）固定在其手腕上。采用深度学习技术对有和没有ASD的儿童进行分类。我们的研究结果表明，学龄儿童与健康成年人相比存在不同的运动运动学特征。与TD儿童相比，ASD儿童表现出较差的手臂运动前馈/反馈控制，表现为运动单元数量更多、运动超调更多以及达到峰值速度/加速度的时间延长。在ASD组中还观察到了独特的运动策略，如更高的速度和加速度。更重要的是，使用多层感知器（MLP）模型，我们在对有和没有ASD的儿童进行分类时显示出约78.1%的准确率。这些发现强调了在目标导向的手臂运动过程中研究上肢运动运动学以及深度学习方法作为有价值工具的潜在用途，以便在进一步验证其在年幼儿童中的特异性后，对ASD进行分类并因此辅助诊断和早期识别。
