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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 |

REPOSITORIES: biostudies-literature

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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.

Project description:BackgroundShifts in eye gaze communicate social information that allows people to respond to another's behavior, interpret motivations driving behavior, and anticipate subsequent behavior. Understanding the social communicative nature of gaze shifts requires the ability to link eye movements and mental state information about objects in the world. Autism spectrum disorder (ASD) is characterized by atypical sensitivity to eye gaze cues, which impacts social communication and relationships. We evaluated whether reduced visual attention to faces explains this difficulty in ASD.MethodsWe employed eye-tracking technology to measure visual attention to faces and gazed-at objects in a 4-alternative forced choice paradigm in adolescents with ASD and typically developing (TD) adolescents. Participants determined the target object that an actor was looking at in ecologically rich scenes. We controlled for group differences in task engagement and data quality.ResultsIn the Gaze Following task, adolescents with ASD were relatively impaired (Cohen's d = 0.63) in the ability to identify the target object. In contrast to predictions, both groups exhibited comparable fixation durations to faces and target objects. Among both groups, individuals who looked longer at the target objects, but not faces, performed better in the task. Finally, among the ASD group, parent SSIS-Social Skills ratings were positively associated with performance on the Gaze Following task. In the Gaze Perception task, there was a similar pattern of results, which provides internal replication of the findings that visual attention to faces is not related to difficulty interpreting eye gaze cues. Together, these findings indicate that adolescents with ASD are capable of following gaze, but have difficulty linking gaze shifts with mental state information.LimitationsAdditional work is necessary to determine whether these findings generalize to individuals across the full autism spectrum. New paradigms that manipulate component processes of eye gaze processing need to be tested to confirm these interpretations.ConclusionsReduced visual attention to faces does not appear to contribute to atypical processing of eye gaze cues among adolescents with ASD. Instead, the difficulty for individuals with ASD is related to understanding the social communicative aspects of eye gaze information, which may not be extracted from visual cues alone.

Project description:OBJECTIVES: To compare the accuracy of pulmonary lobar volumetry using the conventional number of segments method and novel volumetric computer-aided diagnosis using 3D computed tomography images. METHODS: We acquired 50 consecutive preoperative 3D computed tomography examinations for lung tumours reconstructed at 1-mm slice thicknesses. We calculated the lobar volume and the emphysematous lobar volume < -950 HU of each lobe using (i) the slice-by-slice method (reference standard), (ii) number of segments method, and (iii) semi-automatic and (iv) automatic computer-aided diagnosis. We determined Pearson correlation coefficients between the reference standard and the three other methods for lobar volumes and emphysematous lobar volumes. We also compared the relative errors among the three measurement methods. RESULTS: Both semi-automatic and automatic computer-aided diagnosis results were more strongly correlated with the reference standard than the number of segments method. The correlation coefficients for automatic computer-aided diagnosis were slightly lower than those for semi-automatic computer-aided diagnosis because there was one outlier among 50 cases (2%) in the right upper lobe and two outliers among 50 cases (4%) in the other lobes. The number of segments method relative error was significantly greater than those for semi-automatic and automatic computer-aided diagnosis (P < 0.001). The computational time for automatic computer-aided diagnosis was 1/2 to 2/3 than that of semi-automatic computer-aided diagnosis. CONCLUSIONS: A novel lobar volumetry computer-aided diagnosis system could more precisely measure lobar volumes than the conventional number of segments method. Because semi-automatic computer-aided diagnosis and automatic computer-aided diagnosis were complementary, in clinical use, it would be more practical to first measure volumes by automatic computer-aided diagnosis, and then use semi-automatic measurements if automatic computer-aided diagnosis failed.

Project description:Deficits in responding to joint attention (RJA) are early symptoms of autism spectrum disorder (ASD). Currently, no automated tools exist for identifying and quantifying RJA behaviors. A few eye tracking studies have investigated RJA in ASD children but have produced conflicting results. In addition, little is known about the trajectory of RJA development through developmental age. Here, a new video was designed including 12 clips of an actor pointing to or looking at an object. Eye tracking technology was used to monitor RJA in three groups: 143 ASD children assessed with the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) (4-7 years old), 113 age- and gender-matched typically developing children (TDC), and 43 typically developing adults (TDA) (19-32 years old). RJAfinder was developed in R and MATLAB to quantify RJA events from the eye tracking data. RJA events were compared among the three groups. Spearman correlation coefficients between total number of RJA events in ASD and the Social Responsiveness Scale (SRS) scores were calculated. A logistic regression model was built using the average valid sampling rate and the total number of RJA events as two predictive variables to classify ASD and TDC groups. ASD children displayed statistically significantly less RJA events than the TDC and TDA groups with medium-to-large-sized effects. ASD and TDC children both displayed more RJA events in response to pointing stimuli than to looking stimuli. Our logistic regression model predicted ASD tendency with 0.76 accuracy in the testing set. RJA ability improved more slowly between the ages of 4-7 years old in the ASD group than in the TDC group. In ASD children, RJA ability showed negative correlation with SRS total T-score as well as the scores of five subdomains. Our study provides an automated tool for quantifying RJA and insights for the study of RJA in ASD children, which may help improve ASD screening, subtyping, and behavior interventions.

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

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