A Bilingual Child Diagnosed With Autism Spectrum Disorder (ASD)

Silvia and Antonio Juarez described their 4-year-old son Emanuel as often happy. Emanuel came from a bilingual home and spoke both Spanish and English. His favorite activities were reading and playing with his iPad over-and-over again. Reading was a favorite pasttime and something that the Juarez's felt he did well. While he was described as “loving to engage people,” it was not clear that his efforts were successful nor was it clear that they were reciprocated. Briefly observing Emanuel's interaction with his parents, it was noted Emanuel used echolalic phrases and engaged in repetitive and restricted behaviors, toe walking, and finger flicking. These behaviors were noted to be unusual and warranted further evaluation. The Juarez's primary concern was regarding Emanuel's “talking and being able to hold a conversation.” Through speech and language therapy, the family expected that Emanuel's communication skills would improve, and he would be able to participate in conversations and talk in sentences.

Using 2D video-based pose estimation for automated prediction of autism spectrum disorders in young children

Clinical research in autism has recently witnessed promising digital phenotyping results, mainly focused on single feature extraction, such as gaze, head turn on name-calling or visual tracking of the moving object. The main drawback of these studies is the focus on relatively isolated behaviors elicited by largely controlled prompts. We recognize that while the diagnosis process understands the indexing of the specific behaviors, ASD also comes with broad impairments that often transcend single behavioral acts. For instance, the atypical nonverbal behaviors manifest through global patterns of atypical postures and movements, fewer gestures used and often decoupled from visual contact, facial affect, speech. Here, we tested the hypothesis that a deep neural network trained on the non-verbal aspects of social interaction can effectively differentiate between children with ASD and their typically developing peers. Our model achieves an accuracy of 80.9% (F1 score: 0.818; precision: 0.784; recall: 0.854) with the prediction probability positively correlated to the overall level of symptoms of autism in social affect and repetitive and restricted behaviors domain. Provided the non-invasive and affordable nature of computer vision, our approach carries reasonable promises that a reliable machine-learning-based ASD screening may become a reality not too far in the future.

Using 2D Video-based Pose Estimation for Automated Prediction of Autism Spectrum Disorders in Preschoolers

Clinical research in autism has recently witnessed promising digital phenotyping results, mainly focused on single feature extraction, such as gaze, head turn on name-calling or visual tracking of the moving object. The main drawback of these studies is the focus on relatively isolated behaviors elicited by largely controlled prompts. We recognize that while the diagnosis process understands the indexing of the specific behaviors, ASD also comes with broad impairments that often transcend single behavioral acts. For instance, the atypical nonverbal behaviors manifest through global patterns of atypical postures and movements, fewer gestures used and often decoupled from visual contact, facial affect, speech. Here, we tested the hypothesis that a deep neural network trained on the non-verbal aspects of social interaction can effectively differentiate between children with ASD and their typically developing peers. Our model achieves an accuracy of 80.9% (F1 score: 0.818; precision: 0.784; recall: 0.854) with the prediction probability positively correlated to the overall level of symptoms of autism in social affect and repetitive and restricted behaviors domain. Provided the non-invasive and affordable nature of computer vision, our approach carries reasonable promises that a reliable machine-learning-based ASD screening may become a reality not too far in the future.

A Bilingual Child Diagnosed With Autism Spectrum Disorder (ASD)

Silvia and Antonio Juarez described their 4-year-old son Emanuel as often happy. Emanuel came from a bilingual home and spoke both Spanish and English. His favorite activities were reading and playing with his iPad over-and-over again. Reading was a favorite pasttime and something that the Juarez's felt he did well. While he was described as “loving to engage people,” it was not clear that his efforts were successful nor was it clear that they were reciprocated. Briefly observing Emanuel's interaction with his parents, it was noted Emanuel used echolalic phrases and engaged in repetitive and restricted behaviors, toe walking, and finger flicking. These behaviors were noted to be unusual and warranted further evaluation. The Juarez's primary concern was regarding Emanuel's “talking and being able to hold a conversation.” Through speech and language therapy, the family expected that Emanuel's communication skills would improve, and he would be able to participate in conversations and talk in sentences.