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BackgroundReliable and specific biomarkers that can distinguish autism spectrum disorders (ASDs) from commonly co-occurring attention-deficit/hyperactivity disorder (ADHD) are lacking, causing misses and delays in diagnosis, and reducing access to interventions and quality of life. AimsTo examine whether an innovative, brief (1-min), videogame method called Computerised Assessment of Motor Imitation (CAMI), can identify ASD-specific imitation differences compared with neurotypical children and children with ADHD. MethodThis cross-sectional study used CAMI alongside standardised parent-report (Social Responsiveness Scale, Second Edition) and observational measures of autism (Autism Diagnostic Observation Schedule-Second Edition; ADOS-2), ADHD (Conners) and motor ability (Physical and Neurological Examination for Soft Signs). The sample comprised 183 children aged 7–13 years, with ADHD (without ASD), with ASD (with and without ADHD) and who were neurotypical. ResultsRegardless of co-occurring ADHD, children with ASD showed poorer CAMI performance than neurotypical children (P< 0.0001; adjustedR²= 0.28), whereas children with ADHD and neurotypical children showed similar CAMI performance. Receiver operating curve and support vector machine analyses showed that CAMI distinguishes ASD from both neurotypical children (80% true positive rate) and children with ADHD (70% true positive rate), with a high success rate significantly above chance. Among children with ASD, poor CAMI performance was associated with increased autism traits, particularly ADOS-2 measures of social affect and restricted and repetitive behaviours (adjustedR²= 0.23), but not with ADHD traits or motor ability. ConclusionsFour levels of analyses confirm that poor imitation measured by the low-cost and scalable CAMI method specifically distinguishes ASD not only from neurotypical development, but also from commonly co-occurring ADHD. 

Background: Motor imitation difficulties are pervasive in children with Autism Spectrum Disorder (ASD). Previous research demonstrated the validity and reliability of an algorithm called Computerized Assessment of Motor Imitation (CAMI) using 3D depth cameras. However, incorporating CAMI into serious games and making it accessible in clinic and home settings requires a more scalable approach that uses “off-the-shelf” 2D cameras. Method: In a brief (one-minute) task, children (23 ASD, 17 typically developing [TD]) imitated a model’s dance movements while simultaneously being recorded using Kinect Xbox motion tracking technology (Kinect 3D) and a single 2D camera. Pose-estimation software (OpenPose 2D) was used on the 2D camera video to fit a skeleton to the imitating child. Motor imitation scores computed from the fully automated OpenPose 2D CAMI method were compared to scores computed from the Kinect 3D CAMI and Human Observation Coding (HOC) methods. Results: Motor imitation scores obtained from the OpenPose 2D CAMI method were significantly correlated with scores obtained from the Kinect 3D CAMI method (r40 = 0.82, p < 0.001) and the HOC method (r40 = 0.80, p < 0.001). Both 2D and 3D CAMI methods showed better discriminative ability than the HOC, with the Kinect 3D CAMI method outperforming the OpenPose 2D CAMI method (area under ROC curve (AUC): AUCHOC = 0.799, AUC2D-CAMI = 0.876, AUC3D-CAMI= 0.94). Finally, all motor imitation scores were significantly associated with the social communication impairment (all p ≤ 0.003). Conclusions: This pilot-study demonstrated that motor imitation can be automatically quantified using a single 2D camera.