Harnessing AI for Pediatric Developmental Coordination Disorder: Current Insights and Future Directions

Pediatric clinicians are increasingly encountering machine-learning tools that quantify motor function and may improve assessment of developmental coordination disorder (DCD); AI methods in DCD highlight an early emphasis on assessment and early identification. These approaches promise greater measurement consistency across settings and raters, but most work remains early-phase.

Typical methods use supervised learning on movement-derived data from wearables, video capture, and EHR-derived features; some teams apply deep learning for pose estimation and time-series analysis.

Studies are generally small, convenience-sampled, and focused on classification or assessment accuracy rather than therapeutic effect. Reported outcomes emphasize feature extraction, classifier metrics, and signal-derived digital biomarkers—raising questions about clinical readiness when design priority is measurement, not intervention.

Algorithmic extraction of movement features can increase measurement precision and detection sensitivity compared with conventional observational scales, primarily through digital biomarkers and automated marker extraction from video and wearables. Common analytic outputs include derived feature sets, temporal patterns in time-series signals, and pose-estimation summaries mapped to standardized task elements. These quantitatively capture subtle motor-control differences and offer reproducible metrics for monitoring change, yet improved algorithmic performance has not translated into routine clinical decision support.

External validation is limited: most reports rely on internal cross-validation without multicenter replication, underscoring persistent external validation needs for generalizability. Integration with EHRs and clinical workflows is constrained by heterogeneous data formats, privacy requirements, and limited interoperability across sensor platforms and charting systems. Small cohorts and variable diagnostic definitions raise concerns about representativeness and bias, and few trials examine whether AI-informed assessment alters clinical outcomes.

Clinician engagement with rigorous validation studies and multidisciplinary implementation research will clarify the path from algorithmic promise to clinical utility.

Key Takeaways:

• What’s new? Early-phase AI approaches apply supervised learning to movement-based sensors and video to produce objective motor-function measures.
• Who’s affected? Children evaluated for motor coordination concerns and the multidisciplinary clinicians who need reproducible, objective assessment tools.
• What changes next? Wider clinical use depends on multicenter validation, trials that link assessment to interventions, and interoperable workflows that surface device outputs as actionable care signals.