Background Despite the prevalence of Autism Spectrum Disorder (ASD) globally, there's a knowledge gap pertaining to autism in Arabic nations. Recognizing the need for validated biomarkers for ASD, ...our study leverages eye-tracking technology to understand gaze patterns associated with ASD, focusing on joint attention (JA) and atypical gaze patterns during face perception. While previous studies typically evaluate a single eye-tracking metric, our research combines multiple metrics to capture the multidimensional nature of autism, focusing on dwell times on eyes, left facial side, and joint attention. Methods We recorded data from 104 participants (41 neurotypical, mean age: 8.21 + or - 4.12 years; 63 with ASD, mean age 8 + or - 3.89 years). The data collection consisted of a series of visual stimuli of cartoon faces of humans and animals, presented to the participants in a controlled environment. During each stimulus, the eye movements of the participants were recorded and analyzed, extracting metrics such as time to first fixation and dwell time. We then used these data to train a number of machine learning classification algorithms, to determine if these biomarkers can be used to diagnose ASD. Results We found no significant difference in eye-dwell time between autistic and control groups on human or animal eyes. However, autistic individuals focused less on the left side of both human and animal faces, indicating reduced left visual field (LVF) bias. They also showed slower response times and shorter dwell times on congruent objects during joint attention (JA) tasks, indicating diminished reflexive joint attention. No significant difference was found in time spent on incongruent objects during JA tasks. These results suggest potential eye-tracking biomarkers for autism. The best-performing algorithm was the random forest one, which achieved accuracy = 0.76 + or - 0.08, precision = 0.78 + or - 0.13, recall = 0.84 + or - 0.07, and F1 = 0.80 + or - 0.09. Conclusions Although the autism group displayed notable differences in reflexive joint attention and left visual field bias, the dwell time on eyes was not significantly different. Nevertheless, the machine algorithm model trained on these data proved effective at diagnosing ASD, showing the potential of these biomarkers. Our study shows promising results and opens up potential for further exploration in this under-researched geographical context. Keywords: Autism spectrum disorder, Eye-tracking, Face Processing, Prediction, Screening
•The Arabic Sensory Processing Measure shows excellent discriminant validity.•The Arabic Sensory Processing Measure has high internal consistency for the total and subscale scores.•The Arabic Sensory ...Processing Measure’s factor structure is comparable to that of the English version of the measure.
There are few standardized and validated tools to evaluate sensory processing difficulties in Arabic countries. The purpose of this study was to evaluate the psychometric properties of Arabic versions of the Home and Preschool- Home versions of the Sensory Processing Measure(SPM), an instrument widely used for clinical and research purposes.
The overall study sample included 276 children (mean age = 6.7 years; range: 2 to 12), with 192 children (151 males) with a clinical diagnosis of autism spectrum disorder (ASD) and 84 typically developing (TD; 48 males) children. The SPM-Home sub-sample consisted of 117 children and the SPM-Preschool-Home sample consisted of 159 children.
Mean scores of the total SPM-Home and of the subscale scores were significantly (p < .001) higher in the ASD group compared to the TD group, with large effect sizes. Similar results were obtained in the SPM-P-Home for the total score and five of the eight subscales. With one exception, the total scale and the subscales of each measure had good to excellent reliability estimates (median Cronbach's alpha: .86). The factor structure in this sample was consistent with that established in previous studies. The measures also demonstrated good evidence of discriminant validity in ROC analyses.
The findings of this study establish the reliability and validity of the Arabic versions of both the SPM-Home and the SPM-P-Home. The Arabic versions of these measures will enable healthcare professionals to better understand the sensory processing difficulties of their Arabic-speaking clients, including those with ASD.
artificial intelligence is contributing with a huge impact in data analytics. Healthcare is benefiting greatly by using the extensive data available now and it is being analyzed by AI methods ...especially machine learning algorithms. This paper aims to use the data demographic data available and associate it with the Autism questionnaire (Autism Quotient) where we can outline the Autism questioner relevancy to the diagnoses of Autism Spectrum Disorder (ASD). Autism Spectrum Disorder is a developmental and neurological disorder. Autism is also identified as a range of conditions categorized by various challenges such as social skills, repetitive behaviors, speech and non-verbal communication. Autistic children and adults have their unique strengths and differences.
In recent research, Virtual Reality has become well recognized method of delivering therapeutic and educational services. Current technology also provides bimetric objective evidence of attention ...such as eye-tracking and Electrdermal Activity that can inform therapist and educators.A number of relevant papers of VR usability were analyzed to investigate the effectiveness of VR interventions. The study aims to explore Applied Behavior Analysis ABA specific VR interventions potency. It is concluded that VR is an efficient, accessible and successful method of providing ABA interventions.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that begins early in childhood. Children with ASD mainly associated with social interaction and communication deficit in addition to ...behavioral problems. Artificially designed environments are useful to expose ASD individuals to various tasks individualized to target specific goals and objectives. Virtual reality (VR) is an interactive computer-generated experience that stimulate feelings and elicit behaviors. This project aims to design and develop a VR-based application that can be used to improve autistic children's understanding and communication skills and help them to connect with society and the surrounding environment. The results shown are only preliminary but demonstrate the potential of embedding VR technology in autism therapy
Machine learning played an extensive role for development especially in terms of data analytics. Health care is one of the fields where Machine Learning made big phases of enhancements because of the ...huge amount of data being processed and analyzed. This paper aims to implement and compare machine learning techniques to develop a model that can predict Autism Spectrum Disorder (ASD). Autism Spectrum Disorder is a developmental and neurological disorder. Autism is also identified as a range of conditions categorized by various challenges such as social skills, repetitive behaviors, speech and non-verbal communication.Autistic children and adults have their unique strengths and differences.
