Machine learning for distinguishing saudi children with and without autism via eye-tracking data.
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...
| Publicado en: | Child & Adolescent Psychiatry & Mental Health Vol. 17; no. 1; pp. 1 - 10 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
BioMed Central
9/30/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=172443889&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172443889 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17532000 78JH jtl: Child & Adolescent Psychiatry & Mental Health issn: 17532000 maglogo: N pubinfo: dt: 9/30/2023 vid: 17 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 172443889 172443889 172443889 10.1186/s13034-023-00662-3 172443889 ppf: 1 ppct: 9 formats: tig: atl: Machine learning for distinguishing saudi children with and without autism via eye-tracking data. aug: au: Alarifi, Hana Aldhalaan, Hesham Hadjikhani, Nouchine Johnels, Jakob Åsberg Alarifi, Jhan Ascenso, Guido Alabdulaziz, Reem affil: https://ror.org/05n0wgt02 Autism Center, King Faisal Specialists Hospital and Research Center, Riyadh, Kingdom of Saudi Arabia sug: subj: Machine Learning Eye Movements Diagnosis, Differential Autism Spectrum Disorder Diagnosis Autism Spectrum Disorder In Infancy and Childhood Human Autism Spectrum Disorder Epidemiology Biological Markers Saudi Arabia Attention Child, Preschool Child Eye Face Visual Perception Time Factors Algorithms Visual Fields Task Performance and Analysis Precision Random Forest Patient Classification Social Media Male Female Infant Adolescence Adult Software Data Analysis Software Descriptive Statistics Effect Size Funding Source Questionnaires Child, Preschool: 2-5 years Child: 6-12 years Infant: 1-23 months Adolescent: 13-18 years Adult: 19-44 years Male Female ab: 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 ± 4.12 years; 63 with ASD, mean age 8 ± 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 ± 0.08, precision = 0.78 ± 0.13, recall = 0.84 ± 0.07, and F1 = 0.80 ± 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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