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.
CLINICALTRIAL
ClinicalTrials.gov NCT04362982