Intelligent Systems Lab

Author(s): Niki Pandria, Vasileia Petronikolou, Aristotelis Lazaridis, Christos Karapiperis, Eleftherios Kouloumpris, Dimitrios Spachos, Anestis Fachantidis, Dimitrios Vasileiou, Ioannis Vlahavas, Panagiotis Bamidis

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Keywords: adhd, serious games, machine learning, diagnosis, adhd360

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Abstract: Background: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood, however the diagnosis procedure remains challenging as it is non-standardized, multi-parametric and highly dependent on subjective evaluation of the perceived behavior. Objective: To address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (a) early detection of ADHD by assessing the user’s likelihood of having ADHD characteristics and (b) providing complementary training for ADHD management. Methods: A two-phase pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7-16 years. Machine Learning methods were used to detect discriminative gameplay patterns among the two groups (ADHD, non-ADHD) and estimate a player’s likelihood of having ADHD characteristics. Results: A preliminary analysis of collected data showed that the trained models achieve high performance in correctly predicting a user’s label (ADHD or non-ADHD) from his gameplay session in the ADHD360 platform. Conclusions: ADHD360 is characterized by notable capacity to discriminate player gameplay behavior as either ADHD or non-ADHD. Therefore, the ADHD360 platform could be a valuable complementary tool for early ADHD detection. Clinical Trial: ClinicalTrials.gov NCT04362982