TIC-XNet: a structured evidence translation framework for interpretable multimodal pediatric tic event detection with improved temporal alignment and fidelity - Summary - MDSpire
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TIC-XNet: a structured evidence translation framework for interpretable multimodal pediatric tic event detection with improved temporal alignment and fidelity
To develop an interpretable multimodal framework for detecting tic events in children with tic disorders by translating model decisions into structured, time-aligned evidence from synchronized video and physiological signals.
Approach:
Key Findings:
TIC-XNet achieved a window-level AUC of 0.915 ± 0.019 on the pooled shared test set.
It demonstrated higher event-level recall and precision, fewer missed events, and lower post-buffering prediction latency compared to comparator models.
The outputs showed higher decision fidelity, greater stability under perturbation, and closer temporal alignment with expert-annotated tic onsets.
Subject-level translated numerical signals were associated with tic severity.
Interpretation:
Evidence translation can support more interpretable multimodal detection of tic events in children with tic disorders while maintaining strong predictive performance.
Conclusion:
The study indicates that TIC-XNet provides a robust framework for detecting tic events with enhanced interpretability.
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