Dataset Information

Computer-aided autism diagnosis based on visual attention models using eye tracking.

ABSTRACT: An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.

SUBMITTER: Oliveira JS

PROVIDER: S-EPMC8115570 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Computer-aided autism diagnosis based on visual attention models using eye tracking.

Oliveira Jessica S JS Franco Felipe O FO Revers Mirian C MC Silva Andréia F AF Portolese Joana J Brentani Helena H Machado-Lima Ariane A Nunes Fátima L S FLS

Scientific reports 20210512 1

An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual at ...[more]

PMID: 33980874

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Project description:ImportanceIn the US, children with signs of autism often experience more than 1 year of delay before diagnosis and often experience longer delays if they are from racially, ethnically, or economically disadvantaged backgrounds. Most diagnoses are also received without use of standardized diagnostic instruments. To aid in early autism diagnosis, eye-tracking measurement of social visual engagement has shown potential as a performance-based biomarker.ObjectiveTo evaluate the performance of eye-tracking measurement of social visual engagement (index test) relative to expert clinical diagnosis in young children referred to specialty autism clinics.Design, setting, and participantsIn this study of 16- to 30-month-old children enrolled at 6 US specialty centers from April 2018 through May 2019, staff blind to clinical diagnoses used automated devices to measure eye-tracking-based social visual engagement. Expert clinical diagnoses were made using best practice standardized protocols by specialists blind to index test results. This study was completed in a 1-day protocol for each participant.Main outcomes and measuresPrimary outcome measures were test sensitivity and specificity relative to expert clinical diagnosis. Secondary outcome measures were test correlations with expert clinical assessments of social disability, verbal ability, and nonverbal cognitive ability.ResultsEye-tracking measurement of social visual engagement was successful in 475 (95.2%) of the 499 enrolled children (mean [SD] age, 24.1 [4.4] months; 38 [8.0%] were Asian; 37 [7.8%], Black; 352 [74.1%], White; 44 [9.3%], other; and 68 [14.3%], Hispanic). By expert clinical diagnosis, 221 children (46.5%) had autism and 254 (53.5%) did not. In all children, measurement of social visual engagement had sensitivity of 71.0% (95% CI, 64.7% to 76.6%) and specificity of 80.7% (95% CI, 75.4% to 85.1%). In the subgroup of 335 children whose autism diagnosis was certain, sensitivity was 78.0% (95% CI, 70.7% to 83.9%) and specificity was 85.4% (95% CI, 79.5% to 89.8%). Eye-tracking test results correlated with expert clinical assessments of individual levels of social disability (r = -0.75 [95% CI, -0.79 to -0.71]), verbal ability (r = 0.65 [95% CI, 0.59 to 0.70]), and nonverbal cognitive ability (r = 0.65 [95% CI, 0.59 to 0.70]).Conclusions and relevanceIn 16- to 30-month-old children referred to specialty clinics, eye-tracking-based measurement of social visual engagement was predictive of autism diagnoses by clinical experts. Further evaluation of this test's role in early diagnosis and assessment of autism in routine specialty clinic practice is warranted.Trial registrationClinicalTrials.gov Identifier: NCT03469986.

Project description:Background:Visual atypicalities in autism spectrum disorder (ASD) are a well documented phenomenon, beginning as early as 2-6 months of age and manifesting in a significantly decreased attention to the eyes, direct gaze and socially salient information. Early emerging neurobiological deficits in perceiving social stimuli as rewarding or its active avoidance due to the anxiety it entails have been widely purported as potential reasons for this atypicality. Parallel research evidence also points to the significant benefits of animal presence for reducing social anxiety and enhancing social interaction in children with autism. While atypicality in social attention in ASD has been widely substantiated, whether this atypicality persists equally across species types or is confined to humans has not been a key focus of research insofar. Methods:We attempted a comprehensive examination of the differences in visual attention to static images of human and animal faces (40 images; 20 human faces and 20 animal faces) among children with ASD using an eye tracking paradigm. 44 children (ASD n = 21; TD n = 23) participated in the study (10,362 valid observations) across five regions of interest (left eye, right eye, eye region, face and screen). Results:Results obtained revealed significantly greater social attention across human and animal stimuli in typical controls when compared to children with ASD. However in children with ASD, a significantly greater attention allocation was seen to animal faces and eye region and lesser attention to the animal mouth when compared to human faces, indicative of a clear attentional preference to socially salient regions of animal stimuli. The positive attentional bias toward animals was also seen in terms of a significantly greater visual attention to direct gaze in animal images. Conclusion:Our results suggest the possibility that atypicalities in social attention in ASD may not be uniform across species. It adds to the current neural and biomarker evidence base of the potentially greater social reward processing and lesser social anxiety underlying animal stimuli as compared to human stimuli in children with ASD.

Dataset Information

Computer-aided autism diagnosis based on visual attention models using eye tracking.

Publications

Computer-aided autism diagnosis based on visual attention models using eye tracking.

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