Clinical Report: TCM Syndrome Differentiation and Skin Imaging Phenotypes

Overview

This study establishes a connection between Traditional Chinese Medicine (TCM) syndromes and distinct skin imaging phenotypes, demonstrating that these syndromes can predict treatment outcomes. An integrative machine learning model achieved high accuracy in forecasting therapeutic responses based on TCM diagnoses and imaging data.

Background

Traditional Chinese Medicine (TCM) syndrome differentiation is a cornerstone of personalized treatment in dermatology, yet its biological underpinnings remain poorly defined. This lack of objective correlates has hindered the integration of TCM with modern precision medicine. Understanding the relationship between TCM syndromes and quantifiable skin phenotypes could enhance treatment strategies and patient outcomes.

Data Highlights

Key Findings

• Significant differences in skin imaging profiles were observed among the four TCM syndromes (p < 0.001).
• The spleen deficiency with dampness group had the highest median UV spot counts.
• The liver–kidney Yin deficiency group exhibited the most pronounced brown spot intensity.
• The integrative prediction model achieved an AUC of 0.99, indicating exceptional predictive performance.
• SHAP analysis identified UV spot metrics as key predictive features for treatment outcomes.

Clinical Implications

The findings support the use of TCM syndrome differentiation as a valuable tool in dermatology, linking it to objective skin imaging characteristics. Clinicians may consider integrating TCM approaches with modern imaging techniques to enhance personalized treatment plans and improve patient outcomes.

Conclusion

This study provides empirical evidence linking TCM syndromes to specific skin phenotypes, paving the way for a more data-driven approach in dermatological practice. The integration of TCM and quantitative imaging may significantly enhance treatment prediction and personalization.

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