Trial Summary
What is the purpose of this trial?The study will use a non-invasive remote eye-tracking system (Eyelink Portable Duo) to acquire a short series of eye-tracking measures.
Will I have to stop taking my current medications?
The trial information does not specify whether participants need to stop taking their current medications.
What data supports the effectiveness of the treatment Eye Tracking for autism detection?
Eye tracking can effectively distinguish children with autism from typically developing children by analyzing their eye movements and fixation times, achieving a classification accuracy of 85.1%. Additionally, eye tracking has been shown to detect high-functioning autism in adults with around 74% accuracy, indicating its potential as a useful tool for autism detection.
12345Is eye tracking safe for use in humans?
Eye tracking is considered safe for humans as it is non-invasive, meaning it doesn't require surgery or entering the body, and it doesn't involve any harmful procedures.
23467How does eye tracking differ from other treatments for autism?
Eye tracking for autism detection is unique because it uses technology to monitor eye movements and visual processing differences, which can help identify autism without the need for traditional behavioral assessments. This method is non-invasive and can provide early detection, especially in children, by analyzing how they focus on different visual stimuli.
12358Eligibility Criteria
This trial is for young children aged 12-48 months who are patients at Riley Hospital for Children in Indiana. They must have caregivers who speak English or Spanish and can give consent. It's not open to kids younger than 12 months, older than 48 months, or those with non-English/Spanish-speaking caregivers.Inclusion Criteria
My child is between 1 and 4 years old and has an appointment at Riley Hospital.
My child's caregiver speaks English or Spanish.
Children must have a legal guardian that is able to provide consent
Exclusion Criteria
My child is not between 1 and 4 years old.
My caregiver does not speak English or Spanish.
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Developmental Evaluation
Children undergo a standard of care developmental evaluation including a clinical interview and observational measures
1 day
1 visit (in-person)
Eye-Tracking Activity
Participants engage in a one-time eye-tracking activity to view pictures and movies while eye movements are tracked
15 minutes
1 visit (in-person)
Follow-up
Participants are monitored for safety and effectiveness after the eye-tracking activity
4 weeks
Participant Groups
The study is testing a non-invasive eye-tracking system called Eyelink Portable Duo to see if it can help identify the risk of autism in young children during routine health care visits.
1Treatment groups
Experimental Treatment
Group I: Children Undergoing Developmental EvaluationExperimental Treatment1 Intervention
Children undergoing a standard of care developmental evaluation will be enrolled into the study. After the completion of the developmental evaluation, research participation includes a one-time eye-tracking activity in which the child will view a series of different pictures and movies while their eye movements are tracked and recorded.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Riley Hospital for ChildrenIndianapolis, IN
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Who Is Running the Clinical Trial?
Indiana UniversityLead Sponsor
References
Applying Eye Tracking to Identify Autism Spectrum Disorder in Children. [2021]Eye tracking (ET) holds potential for the early detection of autism spectrum disorder (ASD). To overcome the difficulties of working with young children, developing a short and informative paradigm is crucial for ET. We investigated the fixation times of 37 ASD and 37 typically developing (TD) children ages 4-6 watching a 10-second video of a female speaking. ASD children showed significant reductions in fixation time at six areas of interest. Furthermore, discriminant analysis revealed fixation times at the mouth and body could significantly discriminate ASD from TD with a classification accuracy of 85.1%, sensitivity of 86.5%, and specificity of 83.8%. Our study suggests that a short video clip may provide enough information to distinguish ASD from TD children.
The autism biomarkers consortium for clinical trials: evaluation of a battery of candidate eye-tracking biomarkers for use in autism clinical trials. [2023]Eye tracking (ET) is a powerful methodology for studying attentional processes through quantification of eye movements. The precision, usability, and cost-effectiveness of ET render it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder (ASD).
Can pupil size and pupil responses during visual scanning contribute to the diagnosis of autism spectrum disorder in children? [2011]The purpose of this study was to determine whether baseline pupil size and pupil responses during visual scanning with eye-tracking technology could discriminate children with autism spectrum disorder (ASD) from mental age-matched and chronological age-matched controls. To this end, we used stimuli consisting in still color photographs presented centrally to the participant's midline on a stimulus monitor. Each child was presented with a series of neutral faces, virtual faces (avatars) and different objects, separated by black slides. We recorded the mean pupil size and pupil size changes over time in each of the three categories of stimuli and during exposure to the black slides. Fifty-seven children participated in study (19 ASD, mean age 118 months; 19 mental age-matched controls, mean age 87 months; and 19 chronological age-matched controls, mean age 118 months). We compared the baseline pupil size and pupil responses during visual scanning among the three diagnostic groups. During the presentation of slides, the mean pupil size in the ASD group was clearly smaller than in the MA-matched and CA-matched groups. Discriminate analysis of pupil size during the presentation of black slides and slides with visual stimuli successfully predicted group membership in 72% of the participants. Group membership was correctly classified in 89% of the participants in the ASD group, in 63% in the MA-matched group and in 63% in the CA-matched group. These potential biomarkers may contribute to our understanding of the differences in neurological development in the brain in autism and could prove useful as indicators of ASD.
Eye tracking in early autism research. [2021]Eye tracking has the potential to characterize autism at a unique intermediate level, with links 'down' to underlying neurocognitive networks, as well as 'up' to everyday function and dysfunction. Because it is non-invasive and does not require advanced motor responses or language, eye tracking is particularly important for the study of young children and infants. In this article, we review eye tracking studies of young children with autism spectrum disorder (ASD) and children at risk for ASD. Reduced looking time at people and faces, as well as problems with disengagement of attention, appear to be among the earliest signs of ASD, emerging during the first year of life. In toddlers with ASD, altered looking patterns across facial parts such as the eyes and mouth have been found, together with limited orienting to biological motion. We provide a detailed discussion of these and other key findings and highlight methodological opportunities and challenges for eye tracking research of young children with ASD. We conclude that eye tracking can reveal important features of the complex picture of autism.
Detecting High-Functioning Autism in Adults Using Eye Tracking and Machine Learning. [2021]The purpose of this study is to test whether visual processing differences between adults with and without high-functioning autism captured through eye tracking can be used to detect autism. We record the eye movements of adult participants with and without autism while they look for information within web pages. We then use the recorded eye-tracking data to train machine learning classifiers to detect the condition. The data was collected as part of two separate studies involving a total of 71 unique participants (31 with autism and 40 control), which enabled the evaluation of the approach on two separate groups of participants, using different stimuli and tasks. We explore the effects of a number of gaze-based and other variables, showing that autism can be detected automatically with around 74% accuracy. These results confirm that eye-tracking data can be used for the automatic detection of high-functioning autism in adults and that visual processing differences between the two groups exist when processing web pages.
Eye tracking young children with autism. [2021]The rise of accessible commercial eye-tracking systems has fueled a rapid increase in their use in psychological and psychiatric research. By providing a direct, detailed and objective measure of gaze behavior, eye-tracking has become a valuable tool for examining abnormal perceptual strategies in clinical populations and has been used to identify disorder-specific characteristics, promote early identification, and inform treatment. In particular, investigators of autism spectrum disorders (ASD) have benefited from integrating eye-tracking into their research paradigms. Eye-tracking has largely been used in these studies to reveal mechanisms underlying impaired task performance and abnormal brain functioning, particularly during the processing of social information. While older children and adults with ASD comprise the preponderance of research in this area, eye-tracking may be especially useful for studying young children with the disorder as it offers a non-invasive tool for assessing and quantifying early-emerging developmental abnormalities. Implementing eye-tracking with young children with ASD, however, is associated with a number of unique challenges, including issues with compliant behavior resulting from specific task demands and disorder-related psychosocial considerations. In this protocol, we detail methodological considerations for optimizing research design, data acquisition and psychometric analysis while eye-tracking young children with ASD. The provided recommendations are also designed to be more broadly applicable for eye-tracking children with other developmental disabilities. By offering guidelines for best practices in these areas based upon lessons derived from our own work, we hope to help other investigators make sound research design and analysis choices while avoiding common pitfalls that can compromise data acquisition while eye-tracking young children with ASD or other developmental difficulties.
Eye-Tracking in Infants and Young Children at Risk for Autism Spectrum Disorder: A Systematic Review of Visual Stimuli in Experimental Paradigms. [2021]Eye-tracking represents a sensitive, direct measure of gaze allocation and goal-directed looking behaviors that correspond to visual information processing. Clear definitions and standardization of research protocols to document the utility and feasibility of these methods are warranted. This systematic review provides an account of stimuli dimensions and experimental paradigms used in eye-tracking research for young children at risk for ASD published from 2005 through 2019. This review identifies variability in eye-tracking protocols and heterogeneity of stimuli used for eye-tracking as factors that undermine the value of eye-tracking as an objective, reliable screening tool. We underscore the importance of sharing eye-tracking stimuli to enhance replicability of findings and more importantly the need to develop a bank of publicly available, validated stimuli.
In the eye of the beholder: a survey of models for eyes and gaze. [2010]Despite active research and significant progress in the last 30 years, eye detection and tracking remains challenging due to the individuality of eyes, occlusion, variability in scale, location, and light conditions. Data on eye location and details of eye movements have numerous applications and are essential in face detection, biometric identification, and particular human-computer interaction tasks. This paper reviews current progress and state of the art in video-based eye detection and tracking in order to identify promising techniques as well as issues to be further addressed. We present a detailed review of recent eye models and techniques for eye detection and tracking. We also survey methods for gaze estimation and compare them based on their geometric properties and reported accuracies. This review shows that, despite their apparent simplicity, the development of a general eye detection technique involves addressing many challenges, requires further theoretical developments, and is consequently of interest to many other domains problems in computer vision and beyond.