Abstract
BackgroundEarly detection of developmental disabilities in children is essential because early intervention can improve the prognosis of children. Meanwhile, a growing body of evidence has indicated a relationship between developmental disability and motor skill, and thus, motor skill is considered in the early diagnosis of developmental disability. However, there are challenges to assessing motor skill in the diagnosis of developmental disorder, such as a lack of specialists and time constraints, and thus it is commonly conducted through informal questions or surveys to parents.ObjectiveThis study sought to evaluate the possibility of using drag-and-drop data as a digital biomarker and to develop a classification model based on drag-and-drop data with which to classify children with developmental disabilities.MethodsWe collected drag-and-drop data from children with typical development and developmental disabilities from May 1, 2018, to May 1, 2020, via a mobile application (DoBrain). We used touch coordinates and extracted kinetic variables from these coordinates. A deep learning algorithm was developed to predict potential development disabilities in children. For interpretability of the model results, we identified which coordinates contributed to the classification results by applying gradient-weighted class activation mapping.ResultsOf the 370 children in the study, 223 had typical development, and 147 had developmental disabilities. In all games, the number of changes in the acceleration sign based on the direction of progress both in the x- and y-axes showed significant differences between the 2 groups (P<.001; effect size >0.5). The deep learning convolutional neural network model showed that drag-and-drop data can help diagnose developmental disabilities, with an area under the receiving operating characteristics curve of 0.817. A gradient class activation map, which can interpret the results of a deep learning model, was visualized with the game results for specific children.ConclusionsThrough the results of the deep learning model, we confirmed that drag-and-drop data can be a new digital biomarker for the diagnosis of developmental disabilities.
Highlights
Developmental disabilities are a set of common heterogeneous disorders developing in 10%-15% of preschool-age children and characterized by difficulties in one or more domains, including learning, behavior, and self-care [1,2,3]
Detection of developmental disabilities is key because early intervention can improve a child’s prognosis due to rapid brain growth and neuroplasticity [8,9,10]
For detecting children with developmental disabilities, we developed a deep learning classification model based on a 1D convolutional neural network for drag data
Summary
Developmental disabilities are a set of common heterogeneous disorders developing in 10%-15% of preschool-age children and characterized by difficulties in one or more domains, including learning, behavior, and self-care [1,2,3]. It is important to perform continuous clinical examinations and comprehensive tracking for more accurate assessment [15,16,17], poor follow-up adherence rates have been reported This low follow-up rate can induce a loss of chance for early intervention [18]. Objective: This study sought to evaluate the possibility of using drag-and-drop data as a digital biomarker and to develop a classification model based on drag-and-drop data with which to classify children with developmental disabilities. The deep learning convolutional neural network model showed that drag-and-drop data can help diagnose developmental disabilities, with an area under the receiving operating characteristics curve of 0.817. Conclusions: Through the results of the deep learning model, we confirmed that drag-and-drop data can be a new digital biomarker for the diagnosis of developmental disabilities
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