Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

Automated emotion classification could aid those who struggle to recognize emotion, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult affect and therefore underperform when used on child faces. In this study, we designed a strategy to gamify the collection and the labeling of child affect data in an effort to boost the performance of automatic child emotion detection to a level closer to what will be needed for digital healthcare approaches. We leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2,155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion centric database (>30x larger than existing public pediatric affect datasets), we trained a pediatric emotion classification convolutional neural network (CNN) classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions in children. The classifier achieved 66.9% balanced accuracy and 67.4% F1-score on the entirety of CAFE as well as 79.1% balanced accuracy and 78.0% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously published classifiers, the best of which reached 56.0% balanced accuracy even when combining “anger” and “disgust” into a single class. This work validates that mobile games designed for pediatric therapies can generate high volumes of domain-relevant datasets to train state of the art classifiers to perform tasks highly relevant to precision health efforts.Funding Statement: This work was supported in part by funds to DPW from the National Institutes of Health (1R01EB025025-01, 1R21HD091500- 01, 1R01LM013083), the National Science Foundation (Award 2014232), The Hartwell Foundation, Bill and Melinda Gates Foundation, Coulter Foundation, Lucile Packard Foundation, the Weston Havens Foundation, and program grants from Stanford’s Human Centered Artificial Intelligence Program, Stanford’s Precision Health and Integrated Diagnostics Center (PHIND), Stanford’s Beckman Center, Stanford’s Bio-X Center, Predictives and Diagnostics Accelerator (SPADA) Spectrum, Stanford’s Spark Program in Translational Research, and from Stanford’s Wu Tsai Neurosciences Institute’s Neuroscience: Translate Program. We also acknowledge generous support from David Orr, Imma Calvo, Bobby Dekesyer and Peter Sullivan. P.W. would like to acknowledge support from Mr. Schroeder and the Stanford Interdisciplinary Graduate Fellowship (SIGF) as the Schroeder Family Goldman Sachs Graduate Fellow.Declaration of Interests: D.P.W. is the founder of Cognoa.com. This company is developing digital health solutions for pediatric care. A.K. works as part-time consultant to Cognoa.com. All other authors declare no conflict of interests.Ethics Approval Statement: All recruitment and study procedures were approved by the Stanford University Institutional Review Board.

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