Identifying autism spectrum disorder symptoms using response and gaze behavior during the Go/NoGo game CatChicken

Abstract

Previous studies have found that Autism Spectrum Disorder (ASD) children scored lower during a Go/No-Go task and faced difficulty focusing their gaze on the speaker’s face during a conversation. To date, however, there has not been an adequate study examining children’s response and gaze during the Go/No-Go task to distinguish ASD from typical children. We investigated typical and ASD children’s gaze modulation when they played a version of the Go/No-Go game. The proposed system represents the Go and the No-Go stimuli as chicken and cat characters, respectively. It tracks children’s gaze using an eye tracker mounted on the monitor. Statistically significant between-group differences in spatial and auto-regressive temporal gaze-related features for 21 ASD and 31 typical children suggest that ASD children had more unstable gaze modulation during the test. Using the features that differ significantly as inputs, the AdaBoost meta-learning algorithm attained an accuracy rate of 88.6% in differentiating the ASD subjects from the typical ones.

The Role of Intelligent Technologies in Early Detection of Autism Spectrum Disorder (ASD): A Scoping Review

“…[77], [78] findings indicated that ML algorithms could detect ASD with an accuracy greater than 90 percent from a selection of 14 feature items and greater than 80 percent using only three items of Q-CHAT.In addition, VABS (Vineland Adaptive Behavior Scale) [118] daily living normalized zscored [119] assessment scores at 14 months reported AUC of 0.713 [76] for ASD detection. A study by [60] using the eye gaze reported that ASD children exhibited more unstable gaze modulation and demonstrated significantly shorter initial, average, and total fixation durations for social stimuli [56]. Further, [57] suggested that children with ASD show reduced fixation time at the eyes, mouth, and nose, affirming the critical role of fixation on the eyes in detecting autism via eye-tracking.…”

“…Betweenness centrality at four face features, under the right and left eye, left eye, and mouth, was lower in ASD children than TD children by 27, 53, 42, and 61%, respectively, forming a basis of ASD detection. [60] captured the gaze modulation of children with ASD and TD children using an eye tracker as they played a variant of the Go/No-Go game. AdaBoost's meta-learning algorithm could distinguish ASD and non-ASD participants with an accuracy of 88.6% based on gaze patterns.…”

“…There are only three studies that reported greater than 250 participants [74], [77], [78], six studies between 150-250 [47]- [50], [72], [76] and another six studies between 100-150 participants [52], [61], [62], [65], [67], [73]. Seven studies [46], [51], [54], [56], [57], [60], [68] reported enrolment between 50-100 participants and nine studies [44], [45], [55], [58], [59], [64], [69]- [71] represented between 10-50 enrollments. Two studies reported less than 10 participants [53], [66].…”

“…[50]- [52], [54], [55], [57], [60], [67], [68], [70]- [73], [76]- [78] reported psychometric outcomes between 0.7 to 0.9, one study [44] less than 0.7 and ten studies [56], [58], [59], [61]- [66] did not reported any outcomes.…”

Application and research progress of machine learning in the diagnosis and treatment of neurodevelopmental disorders in children

“…Based on the AdaBoost algorithm, the eye tracker was used to track the gaze data of children and construct a distinguishing model for ASD. As a result, the accuracy rate of AdaBoost's algorithm predicting ASD reached 88.60%, which has an application value ( 44 ). The collected the gaze data was huge and complex, and it was difficult to analyze such data with traditional statistical methods, and can only be processed by ML.…”