Phase 3 diagnostic evaluation of a smart tablet serious game to identify autism in 760 children 3–5 years old in Sweden and the United Kingdom

Abstract

IntroductionRecent evidence suggests an underlying movement disruption may be a core component of autism spectrum disorder (ASD) and a new, accessible early biomarker. Mobile smart technologies such as iPads contain inertial movement and touch screen sensors capable of recording subsecond movement patterns during gameplay. A previous pilot study employed machine learning analysis of motor patterns recorded from children 3–5 years old. It identified those with ASD from age-matched and gender-matched controls with 93% accuracy, presenting an attractive assessment method suitable for use in the home, clinic or classroom.Methods and analysisThis is a phase III prospective, diagnostic classification study designed according to the Standards for Reporting Diagnostic Accuracy Studies guidelines. Three cohorts are investigated: children typically developing (TD); children with a clinical diagnosis of ASD and children with a diagnosis of another neurodevelopmental disorder (OND) that is not ASD. The study will be completed in Glasgow, UK and Gothenburg, Sweden. The recruitment target is 760 children (280 TD, 280 ASD and 200 OND). Children play two games on the iPad then a third party data acquisition and analysis algorithm (Play.Care, Harimata) will classify the data as positively or negatively associated with ASD. The results are blind until data collection is complete, when the algorithm’s classification will be compared against medical diagnosis. Furthermore, parents of participants in the ASD and OND groups will complete three questionnaires: Strengths and Difficulties Questionnaire; Early Symptomatic Syndromes Eliciting Neurodevelopmental Clinical Examinations Questionnaire and the Adaptive Behavioural Assessment System-3 or Vineland Adaptive Behavior Scales-II. The primary outcome measure is sensitivity and specificity of Play.Care to differentiate ASD children from TD children. Secondary outcomes measures include the accuracy of Play.Care to differentiate ASD children from OND children.Ethics and disseminationThis study was approved by the West of Scotland Research Ethics Service Committee 3 and the University of Strathclyde Ethics Committee. Results will be disseminated in peer-reviewed publications and at international scientific conferences.Trial registration numberNCT03438994

Digital tools for direct assessment of autism risk during early childhood: A systematic review

“…Therefore, further confirmatory studies are required to test the validity, reliability and specificity of these tools before they can be deployed as screening measures. One group has already started planning Phase 3 trials using large samples in different contexts ( Millar et al, 2019 ), which is an important stride in the right direction. The US Food and Drug Administration (FDA) recently authorised marketing of the Cognoa ASD Diagnosis Aid, software using ML algorithms to help predict the risk of autism based on parent reports, videos of child behaviour and health provider inputs, as an adjunct though not a substitute to the regular diagnostic process.…”

Utilizing Constructed Neural Networks for Autism Screening

“…By integrating ML into SGs, they may become powerful tools for the automated screening and assessment of ASD [32,44]. Studies show that ML in SGs can aid the classification of ASD and help children with early identification, diagnostic assessment, objective metrics, engagement, motivation, and tailored therapy [35,45]. Virtual Reality (VR) adventure SGs can enhance social skills in ASD adolescents with multisensory interactions [46].…”