Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Belen, Ryan Anthony J. de; Bednarz, Tomasz; Sowmya, Arcot; Favero, Dennis Del

doi:10.1038/s41398-020-01015-w

Cited by 93 publications

(55 citation statements)

References 139 publications

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“…AI is well-suited for the quantification of behavioral markers. AI-based technology can further lead to a non-invasive, objective, and automatic tool for autism research [82]. In this perspective, AI-based solutions offer excellent opportunities to explore new subtle features, taking advantage of extracting systematic measurements in a non-intrusive way.…”

Section: Discussionmentioning

confidence: 99%

Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD

Alvari

Furlanello

Venuti

2021

JCM

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Time is a key factor to consider in Autism Spectrum Disorder. Detecting the condition as early as possible is crucial in terms of treatment success. Despite advances in the literature, it is still difficult to identify early markers able to effectively forecast the manifestation of symptoms. Artificial intelligence (AI) provides effective alternatives for behavior screening. To this end, we investigated facial expressions in 18 autistic and 15 typical infants during their first ecological interactions, between 6 and 12 months of age. We employed Openface, an AI-based software designed to systematically analyze facial micro-movements in images in order to extract the subtle dynamics of Social Smiles in unconstrained Home Videos. Reduced frequency and activation intensity of Social Smiles was computed for children with autism. Machine Learning models enabled us to map facial behavior consistently, exposing early differences hardly detectable by non-expert naked eye. This outcome contributes to enhancing the potential of AI as a supportive tool for the clinical framework.

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Section: Discussionmentioning

confidence: 99%

Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD

Alvari

Furlanello

Venuti

2021

JCM

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“…9). Like the emoji cloud visualization used in MeetCues [3], these tools could help a presenter track the changing emotional state of audiences during a meeting, and adapt presentations based on the audience's response, particularly benefiting individuals with neurodevelopmental conditions [28]. Such tools are likely most empowering when they operate in real-time, during an actual meeting, thereby offering high temporal relevance and high environment reality.…”

Section: New Directions For "Empowering" Visualizationsmentioning

confidence: 99%

Perception! Immersion! Empowerment! Superpowers as Inspiration for Visualization

Willett¹,

Aseniero

Carpendale

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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We explore how the lens of fictional superpowers can help characterize how visualizations empower people and provide inspiration for new visualization systems. Researchers and practitioners often tout visualizations' ability to "make the invisible visible" and to "enhance cognitive abilities." Meanwhile superhero comics and other modern fiction often depict characters with similarly fantastic abilities that allow them to see and interpret the world in ways that transcend traditional human perception. We investigate the intersection of these domains, and show how the language of superpowers can be used to characterize existing visualization systems and suggest opportunities for new and empowering ones. We introduce two frameworks: The first characterizes seven underlying mechanisms that form the basis for a variety of visual superpowers portrayed in fiction. The second identifies seven ways in which visualization tools and interfaces can instill a sense of empowerment in the people who use them. Building on these observations, we illustrate a diverse set of "visualization superpowers" and highlight opportunities for the visualization community to create new systems and interactions that empower new experiences with data.Material and illustrations are available under CC-BY 4.0 at osf.io/8yhfz.

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“…If AI enables computers to think, computer vision enables them to see, observe, and understand [ 12 ]. According to the systematic reviews of published studies about computer vision in ASD by de Belen et al [ 13 ] and Rahman et al [ 14 ], no published study has used computer vision technology with deep learning to diagnose ASD using a facial image dataset that met review eligibility criteria. It was emphasized that the current state of computer vision methods applied to ASD research is not well established, amid increasing evidence that suggests that computer vision techniques have a strong impact on autism research [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Screening Autism Spectrum Disorder in Children with Facial Images and Analysis of Ethnoracial Factors in Model Development and Application

Perkowski

2021

Brain Sciences

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Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication, and behavioral challenges. Early intervention for children with ASD can help to improve their intellectual ability and reduces autistic symptoms. Multiple clinical researches have suggested that facial phenotypic differences exist between ASD children and typically developing (TD) children. In this research, we propose a practical ASD screening solution using facial images through applying VGG16 transfer learning-based deep learning to a unique ASD dataset of clinically diagnosed children that we collected. Our model produced a 95% classification accuracy and 0.95 F1-score. The only other reported study using facial images to detect ASD was based on the Kaggle ASD Facial Image Dataset, which is an internet search-produced, low-quality, and low-fidelity dataset. Our results support the clinical findings of facial feature differences between children with ASD and TD children. The high F1-score achieved indicates that it is viable to use deep learning models to screen children with ASD. We concluded that the racial and ethnic-related factors in deep-learning based ASD screening with facial images are critical to solution viability and accuracy.

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Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Cited by 93 publications

References 139 publications

Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD

Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD

Perception! Immersion! Empowerment! Superpowers as Inspiration for Visualization

Deep Learning Approach for Screening Autism Spectrum Disorder in Children with Facial Images and Analysis of Ethnoracial Factors in Model Development and Application

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