Development of a prediction model for detecting developmental disabilities in preschool-age children through digital biomarker-driven deep learning in serious games: a retrospective study (Preprint)
BACKGROUND Early detection of developmental disabilities in children is essential because early intervention can improve the prognosis of children due to rapid growth and neuroplasticity. Given the phenotypical nature of developmental disabilities, high variability may come from the assessment process. Because there is a growing body of evidence indicating a relationship between developmental disability and motor, motor skill is considered as a factor to facilitate early diagnosis of developmental disability. However, there are problems to capture their motor skill, such as lack of specialists and time constraints, in the diagnosis of developmental disorders, which is conducted through informal questions or surveys to their parents. OBJECTIVE This study aimed to 1) identify the possibility of drag-and-drop data as a digital biomarker and 2) develop a classification model based on drag-and-drop data to classify children with developmental disabilities. METHODS We collected the drag-and-drop data of children with normal and abnormal development from May 1, 2018, to May 1, 2020, in a mobile application (DoBrain). In this study, 223 normal development and 147 developmental disabled children were involved. We used touch coordinates and extracted kinetic variables from these coordinates. A deep learning algorithm was developed to predicted to classify children with development. For the interpretability of the model result, we identified which coordinates contribute the classification results by conducting the Grad-CAM. RESULTS Of the 370 children in the study, 223 had normal development, and 147 had developmental disabilities were included. In all games, the number of changes in the acceleration sign based on the direction of progress both in x, and y-axis showed significant differences between the two groups (p<0.001 and es>0.5, respectively). The deep learning convolutional neural network model showed that drag-and-drop data can help diagnose developmental disabilities with a sensitivity of 0.71 and specificity of 0.78. Grad class activation map, which can interpret the results of the deep learning model, was visualized with the game results of specific children. CONCLUSIONS Through the results of the deep learning model, it was confirmed that the drag-and-drop data can be a new digital biomarker for the diagnosis of developmental disabilities.