Automated Detection of Neurodevelopmental Disorders Using Face-to-Face Mobile Technology Among Typically Developing Greek Children: Randomized Controlled Trial

Abstract

Background: Neurodevelopmental disorders (NDs) are characterized by heterogeneity, complexity, and interactions among multiple domains with long-lasting effects in adulthood. Early and accurate identification of children at risk for NDs is crucial for timely intervention, yet many cases remain undiagnosed, leading to missed opportunities for effective interventions. Digital tools can help clinicians assist and identify NDs. The concept of using serious games to enhance health care has gained attention among a growing group of scientists, entrepreneurs, and clinicians.

Objective: This study aims to explore the core principles of automated mobile detection of NDs in typically developing Greek children, using a serious game developed within the SmartSpeech project, designed to evaluate multiple developmental domains through principal component analysis (PCA).

Methods: A total of 229 typically developing children aged 4 to 12 years participated in the study. The recruitment process involved open calls through public and private health and educational institutions across Greece. Parents were thoroughly informed about the study's objectives and procedures, and written consent was obtained. Children engaged under the clinician's face-to-face supervision with the serious game "Apsou," which assesses 18 developmental domains, including speech, language, psychomotor, cognitive, psychoemotional, and hearing abilities. Data from the children's interactions were analyzed using PCA to identify key components and underlying principles of ND detection.

Results: A sample of 229 typically developing preschoolers and early school-aged children played the Apsou mobile serious game for automated detection of NDs. Performing a PCA, the findings identified 5 main components accounting for about 80% of the data variability that potentially have significant prognostic implications for a safe diagnosis of NDs. Varimax rotation explained 61.44% of the total variance. The results underscore key theoretical principles crucial for the automated detection of NDs. These principles encompass communication skills, speech and language development, vocal processing, cognitive skills and sensory functions, and visual-spatial skills. These components align with the theoretical principles of child development and provide a robust framework for automated ND detection.

Conclusions: The study highlights the feasibility and effectiveness of using serious games for early ND detection in children. The identified principal components offer valuable insights into critical developmental domains, paving the way for the development of advanced machine learning applications to support highly accurate predictions and classifications for automated screening, diagnosis, prognosis, or intervention planning in ND clinical decision-making. Future research should focus on validating these findings across diverse populations integrating additional features such as biometric data and longitudinal tracking to enhance the accuracy and reliability of automated detection systems.

Trial registration: ClinicalTrials.gov NCT06633874; https://clinicaltrials.gov/study/NCT06633874.

International registered report identifier (irrid): RR2-https://doi.org/10.3390/signals4020021.

Keywords: Greece; apps; assessment; automated detection; child; cognitive; detection; digital tool; early intervention; early screening; hearing; machine learning; main principles; neurodevelopmental disorders; predictions; principal component analysis; prognosis; psychoemotional; psychomotor; screening; serious game; speech.

Figure 1

DSM-5 visualization of included NDs profile patterns. ADHD: attention-deficit/hyperactivity disorder; ASD: autism spectrum disorder; CD: communication disorder; DSM-5: Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition); ID: intellectual disability; ND: neurodevelopmental disorder; SLD: specific learning disorder.

Figure 2

The study’s CONSORT flow diagram.

Figure 3

Eigenvalues of 5 main PCA in a scree plot of NDs measured by the SG “Apsou.” ND: neurodevelopmental disorder; PCA: principal component analysis; SG: serious game.