Ho Siu Cheong, Chen Yiliang, Xie Yao Jie, Yeung Wing-Fai, Chen Shu-Cheng, Qin Jing
School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.
Front Artif Intell. 2025 Jan 6;7:1501184. doi: 10.3389/frai.2024.1501184. eCollection 2024.
Traditional Chinese medicine (TCM) has long utilized tongue diagnosis as a crucial method for assessing internal visceral condition. This study aims to modernize this ancient practice by developing an automated system for analyzing tongue images in relation to the five organs, corresponding to the heart, liver, spleen, lung, and kidney-collectively known as the "five viscera" in TCM. We propose a novel tongue image partitioning algorithm that divides the tongue into four regions associated with these specific organs, according to TCM principles. These partitioned regions are then processed by our newly developed OrganNet, a specialized neural network designed to focus on organ-specific features. Our method simulates the TCM diagnostic process while leveraging modern machine learning techniques. To support this research, we have created a comprehensive tongue image dataset specifically tailored for these five visceral pattern assessment. Results demonstrate the effectiveness of our approach in accurately identifying correlations between tongue regions and visceral conditions. This study bridges TCM practices with contemporary technology, potentially enhancing diagnostic accuracy and efficiency in both TCM and modern medical contexts.
中医长期以来一直将舌诊作为评估内脏状况的关键方法。本研究旨在通过开发一个自动化系统来分析与心、肝、脾、肺、肾（中医统称为“五脏”）这五个器官相关的舌图像，使这一古老的实践现代化。我们提出了一种新颖的舌图像分割算法，根据中医理论将舌头分为与这些特定器官相关的四个区域。然后，这些分割后的区域由我们新开发的OrganNet进行处理，OrganNet是一个专门设计用于关注器官特定特征的神经网络。我们的方法在利用现代机器学习技术的同时，模拟了中医诊断过程。为支持这项研究，我们创建了一个专门针对这五种内脏模式评估的综合舌图像数据集。结果表明，我们的方法在准确识别舌区与内脏状况之间的相关性方面是有效的。这项研究将中医实践与当代技术相结合，有可能提高中医和现代医学背景下的诊断准确性和效率。
