Modeling epithelial wound closure dynamics with AI: A comparative study across cell types.

Background: Skin wound healing exhibits complex spatiotemporal heterogeneity that challenges traditional static assessment methods. Current artificial intelligence (AI) approaches often treat segmentation and temporal modeling as disconnected processes, limiting dynamic quantification of healing trajectories across distinct cell types. We aim to develop an integrated AI framework combining enhanced segmentation with temporal modeling for quantifying Methods: We implemented an enhanced UNet++ model for wound segmentation in time-lapse images, benchmarked against Otsu thresholding. Temporal closure trajectories were modeled using polynomial regression, Random Forest (RF), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), and Temporal Convolutional Network (TCN). Performance was evaluated via Dice/IoU (segmentation) and MAE/R Results: UNet++ achieved significantly higher segmentation accuracy than Otsu thresholding (Dice: Conclusion: This integrated framework enables precise dynamic wound monitoring, holding clinical potential for chronic ulcer management and tumor margin surveillance, particularly through its ability to discern cell-type-specific healing phenotypes.