Gazefinder-based measures may provide adjunctive information to support screening and referral-related decision-making for clinically defined severe ASD in young children, suggesting that Gazefinder-based measures may provide adjunctive information to support screening and referral-related decision-making for clinically defined severe ASD in young children.
Abstract
Delays in diagnostic confirmation remain common in young children with autism spectrum disorder (ASD). These delays are particularly concerning for children with severe symptoms and elevated support needs, for whom early identification is especially important. There is therefore a need for objective and feasible approaches to assist early identification prior to specialist evaluation. Eye-tracking is a non-invasive method for quantifying gaze-fixation patterns associated with ASD. The present study examined whether gaze-fixation indices derived from the Gazefinder eye-tracking system can identify a clinically defined severe ASD subgroup within a real-world clinical population. The analysis included 442 children aged 2-6 years referred to a child psychiatry outpatient clinic who underwent Gazefinder assessment. Based on Childhood Autism Rating Scale (CARS) scores, children were classified into a Severe ASD group (n = 42) and an Other group (Non-ASD and Mild-to-moderate ASD; n = 400). Gaze fixation rates on predefined regions of interest were compared, and discriminative performance was evaluated using receiver operating characteristic analyses. Children in the Severe ASD group exhibited reduced fixation on the mouth region in dynamic facial stimuli and reduced fixation on people relative to geometry. A composite criterion derived from four gaze-fixation indices yielded a sensitivity of 85.7% and a specificity of 55.3% for discriminating Severe ASD. These findings suggest that Gazefinder-based measures may provide adjunctive information to support screening and referral-related decision-making for clinically defined severe ASD in young children.
It is argued that future early ASD detection systems should be developed as clinician-supervised decision-support tools rather than autonomous diagnostic instruments.
Wenhao Luo, Z. Yin, Jianbiao Dai· Diagnostics· 0 citations
Behavioral observation through examiner-child structured interactive play, particularly metrics related to eye contact, effectively differentiates toddlers with iASD.
Dan Ai, Binyue Hu, Qiuhong Wei et al.· BMC Psychiatry· 0 citations
Auditory behavior reflects an individual's response to environmental sounds. Atypical responses to auditory input may indicate developmental difficulties, particularly in children with autism spectrum disorder (ASD). This study aimed to identify auditory behavior in children with ASD in relation to autism severity, age, treatment onset, and history of ear disease. The sample included 30 children with ASD (21 female and 9 male), classified into three groups according to ASD severity: mild (n = 3), moderate (n = 4), and severe (n = 23). Auditory behavior was assessed using the Questionnaire on Auditory Behavior for Children with Autism Spectrum Disorder (QAB-ASD), and ASD severity was determined using the Childhood Autism Rating Scale (CARS). Initial analyses suggested differences in auditory behavior according to ASD severity, with children in the mild ASD group showing higher auditory behavior scores than those with more severe ASD. However, these differences did not remain statistically significant after Bonferroni correction. No statistically significant differences were found in relation to treatment onset, history of ear diseases, or age. Given the small, uneven sample, the findings should be interpreted with caution. The findings highlight the need for comprehensive assessment of auditory behavior when planning individualized interventions for children with ASD.
OBJECTIVE
To investigate positive results on a screening instrument for autism spectrum disorder (pos-ASDS) and associated neonatal and early developmental risk factors in school-age children born extremely preterm (EPT).
STUDY DESIGN
Researchers assessed 380 EPT children (GA 24 to <28 weeks) at 6-7 years-of-age using the Social Communication Questionnaire (SCQ). Pos-ASDS was defined as an SCQ score ≥15. Logistic regression examined independent associations of perinatal factors and 18-22 months behavior and cognitive scores with pos-ASDS.
RESULTS
Fourteen % of the children scored ≥ 15 in the ASDS (SCQ). Risk factors included male sex, lower birth weight, severe retinopathy of prematurity, motor/sensory impairments, lower cognitive scores, poorer emotional competence, and more behavioral problems at 18-22 months. Neonatal MRI and cranial ultrasound findings were not significantly different.
CONCLUSION
Children born EPT have increased positive ASDS. It is important to assess their socioemotional development early and pos-ASDS risk factors to facilitate prompt intervention.
Myriam Peralta-Carcelen, S. Hintz, Carla M. Bann et al.· Journal of Perinatology· 0 citations
Artificial intelligence shows promise as a supportive tool for early screening, but current evidence supports its use as a complement to, rather than replacement for, clinical assessment.
Andrea Catalina Mahecha Ballesteros, Juanita Valeria García Bello, Eleaine Scarlet González Zuñiga et al.· Current Psychiatry Reports· 0 citations
In clinical practice, objectively distinguishing children with autism spectrum disorder (ASD) from those with typical development (TD) and other neurodevelopmental conditions with overlapping symptoms, such as global developmental delay (GDD), is critical for improving developmental outcomes. This study aimed to investigate the feasibility of eye-tracking-based machine learning (ML) models in distinguishing children with developmental concerns (ASD + GDD) from TD children, and in further differentiating ASD from GDD. A total of 168 toddlers (45 ASD, 56 GDD, and 67 TD; aged 18-48 months) viewed a socially dynamic "hide-and-seek" video. Both conventional area-of-interest (AOI)-based features and combined features derived from AOI combinations were used to train five ML classifiers. A two-stage classification framework was adopted, and SHapley Additive exPlanations (SHAP) analysis was used to interpret feature contributions. Combined features consistently outperformed isolated AOI features. For TD versus clinical classification, the random forest model achieved the highest accuracy of 80.36% (sensitivity = 84.16%, specificity = 74.63%). In differentiating ASD from GDD, the k-Nearest Neighbors model achieved the highest accuracy of 79.21% (sensitivity = 71.11%, specificity = 85.71%). SHAP analysis indicated that cross-regional attention features contributed substantially to model performance. These findings suggest that eye-tracking-based ML models may provide a promising approach for early screening of developmental conditions and ASD-GDD differentiation. Combined features reflecting cross-regional attention distribution demonstrated higher discriminative value than conventional AOI measures. Future studies should validate these findings in larger, multicenter, and more balanced samples with richer dynamic eye-tracking representations.
Gang Zhou, Xiaobin Zhang, Xingda Qu et al.· Research in Developmental Di...· 0 citations
