A portable non-contact tongue imaging system with automated analysis for community and home settings

This is the authors' abstract. We don't add key points for this paper.

Background: Traditional tongue inspection relies on visual assessment by practitioners, which introduces subjectivity and compromises reproducibility. Existing solutions often rely on enclosed, dedicated acquisition instruments with nontrivial operation, whereas mobile self-capture approaches are more accessible but sensitive to environmental variability, making reliable analysis challenging in real-world use. Objective: To develop a portable non-contact tongue imaging and automated analysis system that is robust to real-world acquisition variability. Methods: We designed a portable acquisition terminal that integrates a camera, touchscreen preview, touch-initiated capture with voice prompts, and supplementary illumination for acquisition assistance. For automated analysis, we developed TongueSegNet (TSegNet) for tongue segmentation, incorporating stage-dependent residual modulation, deep-stage attention enhancement, and gated skip-pathway feature fusion to improve feature representation and boundary delineation. For fissured-tongue feature recognition, we developed Residual Kolmogorov-Arnold Network (ResKAN), which combines a convolutional neural network feature extractor with a Kolmogorov-Arnold Network-based head to improve modelling capacity for fine-grained texture patterns. Results: On tongue images acquired under unconstrained conditions, TSegNet achieved mean Dice of 98.16%, mean intersection over union of 96.42%, and mean pixel accuracy of 98.31%, outperforming representative baselines. ResKAN achieved mean accuracy of 92.48%, sensitivity of 92.67%, specificity of 92.31%, and a fissured-class F1 score of 92.34%. Conclusion: The proposed system enables reliable non-contact tongue imaging with automated server-side analysis under unconstrained conditions. These findings support the feasibility of this integrated approach as an initial step toward more accessible automated tongue-image analysis in community and home settings.