AI Summary & Key Takeaways
AI InsightConclu-sion: Computational identification of facial behavior in children a promising beginning for the technologies to aid psychiatrists in the evaluation of learning and other neurodevelopmental disorders.Quantitative assessment of faci-al expression in neurodevelopmental disorders are both beneficial and informative and in future may be used as an addition to traditional methods of psychiatric examination.
📌 3 Key Takeaways
Özet
Objective: Clinical characterization of facial behavior is getting more critical in psychiatric disorders; however, there are no objective measures of these expressions. Our study aims to investigate to affective facial behaviors in specific learning disorder (SLD), collect objective facial behavior information to help decision making, and examine the discrimination ability of these behaviors in SLD and healthy controls (HC). Methods: SLD and HC group watched three, 5-minute scenes from cartoon videos and between these scenes in 2-minute question session was applied. Openface software for video analysis used three machine learning algorithms and the performance of these algorithms tested on our data using SLD and HC groups as prediction class. ROC curves and AUC had been calcu-lated. Results:Prediction models using three machine learning classifiers had been created independently with tenfold cross-validation. SVM method showed the highest AUC=0.76 with sensitivity 72%, specifity 96%. Conclu-sion: Computational identification of facial behavior in children a promising beginning for the technologies to aid psychiatrists in the evaluation of learning and other neurodevelopmental disorders.Quantitative assessment of faci-al expression in neurodevelopmental disorders are both beneficial and informative and in future may be used as an addition to traditional methods of psychiatric examination.