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Automated Detection of Neurodevelopmental Disorders Using Face-to-Face Mobile Technology Among Typically Developing

Eugenia I Toki1,2, Victoria Zakopoulou1, Giorgos Tatsis1

  • 1Department of Speech and Language Therapy, School of Health Sciences, University of Ioannina, Ioannina, Greece.

JMIR Formative Research
|October 11, 2024
PubMed
Summary

Serious games show promise for early detection of neurodevelopmental disorders (NDs) in children. This study identified key developmental components using a mobile game, aiding automated screening and diagnosis.

Keywords:
Greeceappsassessmentautomated detectionchildcognitivedetectiondigital toolearly interventionearly screeninghearingmachine learningmain principlesneurodevelopmental disorderspredictionsprincipal component analysisprognosispsychoemotionalpsychomotorscreeningserious gamespeech

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Area of Science:

  • Pediatric Neurodevelopment
  • Digital Health Technologies
  • Machine Learning in Healthcare

Background:

  • Neurodevelopmental disorders (NDs) present complex challenges, often leading to delayed diagnosis and missed intervention opportunities.
  • Early identification is critical for effective management and long-term outcomes in children.
  • Digital tools, particularly serious games, are emerging as valuable aids for clinicians in identifying NDs.

Purpose of the Study:

  • To explore core principles for automated mobile detection of NDs in typically developing Greek children.
  • To evaluate multiple developmental domains using a serious game within the SmartSpeech project.
  • To apply Principal Component Analysis (PCA) for identifying key detection principles.

Main Methods:

  • 229 typically developing children (ages 4-12) participated.
  • Children used the 'Apsou' mobile serious game under clinical supervision, assessing 18 developmental domains.
  • Principal Component Analysis (PCA) was employed to analyze game interaction data.

Main Results:

  • Five principal components, explaining ~80% of data variability, were identified for ND detection.
  • Key developmental principles include communication, speech/language, cognitive, sensory, and visual-spatial skills.
  • Findings support a robust framework for automated ND detection.

Conclusions:

  • Serious games are feasible and effective for early ND detection.
  • Identified components offer insights for developing advanced machine learning applications for ND screening and diagnosis.
  • Future research should validate findings in diverse populations and integrate additional data for enhanced accuracy.