Healthcare
Artificial Intelligence in the Evaluation and Intervention of Developmental Coordination Disorder: A Scoping Review of Methods, Clinical Purposes, and Future Directions
This scoping review explores the nascent application of AI in Developmental Coordination Disorder (DCD), revealing a predominant focus on screening and assessment over therapeutic intervention. Key findings indicate that AI primarily leverages supervised machine learning on movement-based data for early identification and motor assessment. However, significant gaps exist in multimodal approaches, clinical translation, and intervention development. Future research should prioritize clinically integrated, OT- and PT-centered AI tools for personalized intervention and functional outcomes.
Executive Impact: Key Metrics
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Studies Included
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Screening & Assessment
Studies focusing on early identification, diagnosis support, and automated motor assessment of DCD.
- Buettner et al. [35]
- Letts et al. [37]
- Dai et al. [38]
- Brons et al. [36]
- Tang et al. [41]
Intervention & Rehabilitation
Research applying AI to support therapeutic interventions and monitor rehabilitation progress in DCD.
- Demirci et al. [39]
- Marhraoui et al. [40]
7
Total Studies Included
Reflecting the early stage of AI research in DCD.
AI Application Workflow in DCD Research
Data Collection (Movement, EEG, EHR)
→
AI Model Development (ML, DL, CV)
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Evaluation (Assessment, Classification, Screening)
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Intervention (Limited but Emerging)
| AI Method | Primary Clinical Purpose |
|---|---|
| Supervised ML (Random Forest, SVM) |
|
| Deep Learning (CNN, LSTM) |
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| Computer Vision (Markerless Video) |
|
AI-Assisted Occupational Therapy for Handwriting
Demirci et al. [39] conducted a randomized controlled trial. They used an ML-driven adaptive feedback system on a digital handwriting platform.
Outcome: Significant improvements were observed in all handwriting domains (legibility, speed, spacing, alignment) in the AI-assisted group compared to controls. This highlights the potential of AI for direct therapeutic intervention, not just assessment.
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Randomized Controlled Trial Identified
Out of 7 studies, only one was an RCT, underscoring the limited clinical effectiveness evidence.
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Your AI Implementation Roadmap
A strategic overview of key phases for successful AI integration, inspired by the research findings.
Phase 1: Needs Assessment & Data Collection
Identify specific DCD assessment/intervention gaps. Collect diverse, high-quality data (motor, neurophysiological, EHR) from relevant populations, ensuring ethical guidelines and privacy.
Phase 2: AI Model Development & Validation
Develop and refine AI models (ML, Deep Learning, Multimodal) tailored to specific DCD contexts. Conduct rigorous internal and external validation using independent datasets and diverse populations.
Phase 3: Clinical Integration & Usability Testing
Integrate AI tools into existing clinical workflows (OT/PT). Focus on clinician interpretability, usability, and adaptive feedback mechanisms. Conduct feasibility and pilot studies in real-world settings.
Phase 4: Longitudinal Effectiveness & Impact Evaluation
Perform randomized controlled trials to evaluate AI-assisted interventions. Assess long-term functional outcomes, patient satisfaction, and economic impact. Prioritize adaptive and personalized DCD care pathways.
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