Automated Detection of Neurodevelopmental Disorders Using Mobile Technology
Overview
Neurodevelopmental disorders (NDs) can have complex effects on children and may persist into adulthood. Identifying these disorders early is essential for effective intervention, yet many children go undiagnosed. Digital tools, especially serious games, offer innovative ways for clinicians to detect NDs.
Study Goals
This study investigates how mobile technology can automate the detection of NDs in normally developing Greek children using a serious game called “Apsou.” This game evaluates various developmental areas through data analysis.
Methodology
We studied 229 children aged 4 to 12 years. Participants were recruited from health and educational institutions in Greece, with parents fully informed and giving consent. Under a clinician’s supervision, children played “Apsou,” which assesses 18 different developmental areas like speech, language, and cognitive skills. We analyzed their performance to identify key indicators of NDs.
Key Findings
The study revealed five main components from the data that can help in diagnosing NDs effectively, explaining 80% of the variability observed. These components include:
- Communication skills
- Speech and language development
- Cognitive abilities
- Sensory functions
- Visual-spatial skills
Conclusion
The research demonstrates that serious games can effectively assist in the early detection of NDs. The identified components provide a solid foundation for developing advanced machine learning tools that can enhance automated screening and diagnosis processes. Future studies should validate these findings in various populations and incorporate additional features for better accuracy.
Clinical Trials Registration
ClinicalTrials.gov NCT06633874; ClinicalTrials.
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