Screening children at risk for developmental disabilities based on face landmark from video data of mobile-based application: Cross-Sectional Study (Preprint)
التفاصيل البيبلوغرافية
العنوان:
Screening children at risk for developmental disabilities based on face landmark from video data of mobile-based application: Cross-Sectional Study (Preprint)
BACKGROUND Early detection and intervention of developmental disabilities (DDs) are critical for improving the long-term outcomes of the afflicted children. Mobile-based applications are easily accessible and may thus help the early identification of DDs. OBJECTIVE We aimed to identify facial expression and head pose based on face landmark data extracted from face recording videos and to differentiate the characteristics between children with DDs and those without. METHODS Eighty-nine children (DD, n=33; typically developing, n=56) were included in the analysis. Using the mobile-based application, we extracted facial landmarks and head poses from the recorded videos and performed Long Short-Term Memory(LSTM)-based DD classification. RESULTS Stratified k-fold cross-validation showed that the average values of accuracy, precision, recall, and f1-score of the LSTM based deep learning model of DD children were 88%, 91%,72%, and 80%, respectively. Through the interpretation of prediction results using SHapley Additive exPlanations (SHAP), we confirmed that the nodding head angle variable was the most important variable. All of the top 10 variables of importance had significant differences in the distribution between children with DDs and those without (p CONCLUSIONS Our results provide preliminary evidence that the deep-learning classification model using mobile-based children’s video data could be used for the early detection of children with DDs.
