Visceral condition assessment through digital tongue image analysis.

Area of Science:

• Computational Medicine
• Artificial Intelligence in Healthcare
• Traditional Chinese Medicine Diagnostics

Background:

• Traditional Chinese Medicine (TCM) employs tongue diagnosis to evaluate internal organ health.
• Existing diagnostic methods lack automation and objective quantification.
• There is a need to integrate TCM principles with modern technology for enhanced accuracy.

Purpose of the Study:

• To develop an automated system for tongue image analysis based on TCM's five viscera (heart, liver, spleen, lung, kidney).
• To create a novel algorithm for partitioning tongue images into organ-specific regions.
• To build and utilize a specialized neural network (OrganNet) for analyzing these regions.

Main Methods:

• Development of a novel tongue image partitioning algorithm based on TCM principles.
• Creation of OrganNet, a specialized neural network for organ-specific feature analysis.
• Compilation of a comprehensive tongue image dataset for five visceral pattern assessment.

Main Results:

• The proposed tongue image partitioning algorithm effectively divides the tongue into TCM-defined organ regions.
• OrganNet demonstrated effectiveness in analyzing partitioned tongue regions for visceral condition assessment.
• Accurate correlations were identified between specific tongue regions and visceral patterns.

Conclusions:

• The developed automated system successfully bridges TCM tongue diagnosis with modern machine learning.
• This approach holds potential for improving diagnostic accuracy and efficiency in both TCM and conventional medicine.
• The study validates the integration of AI for objective assessment of TCM diagnostic indicators.