Department of Chinese Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan.
School of Post-Baccalaureate Chinese Medicine, Tzu Chi University, Hualien 97048, Taiwan.
Comput Math Methods Med. 2022 Nov 7;2022:3545712. doi: 10.1155/2022/3545712. eCollection 2022.
Tongue diagnosis, a noninvasive examination, is an essential step for syndrome differentiation and treatment in traditional Chinese medicine (TCM). Sublingual vein (SV) is examined to determine the presence of blood stasis and blood stasis syndrome. Many studies have shown that the degree of SV stasis positively correlates with disease severity. However, the diagnoses of SV examination are often subjective because they are influenced by factors such as physicians' experience and color perception, resulting in different interpretations. Therefore, objective and scientific diagnostic approaches are required to determine the severity of sublingual varices. This study aims at developing a computer-assisted system based on machine learning (ML) techniques for diagnosing the severity of sublingual varicose veins. We conducted a comparative study of the performance of several supervised ML models, including the support vendor machine, K-neighbor, decision tree, linear regression, and Ridge classifier and their variants. The main task was to differentiate sublingual varices into mild and severe by using images of patients' SVs. To improve diagnostic accuracy and to accelerate the training process, we proposed using two model reduction techniques, namely, the principal component analysis in conjunction with the slice inverse regression and the convolution neural network (CNN), to extract valuable features during the preprocessing of data. Our results showed that these two extraction methods can reduce the training time for the ML methods, and the Ridge-CNN method can achieve an accuracy rate as high as 87.5%, which is similar to that of experienced TCM physicians. This computer-aided tool can be used for reference clinical diagnosis. Furthermore, it can be employed by junior physicians to learn and to use in clinical settings.
舌诊是一种非侵入性的检查方法，是中医辨证施治的重要步骤。通过观察舌下静脉（SV），可以判断是否存在血瘀和血瘀证。许多研究表明，SV 淤血的程度与疾病的严重程度呈正相关。然而，SV 检查的诊断往往是主观的，因为它们受到医生经验和颜色感知等因素的影响，导致不同的解释。因此，需要客观和科学的诊断方法来确定舌下静脉曲张的严重程度。本研究旨在开发一种基于机器学习（ML）技术的计算机辅助系统，用于诊断舌下静脉曲张的严重程度。我们对几种监督 ML 模型的性能进行了比较研究，包括支持向量机、K-近邻、决策树、线性回归和 Ridge 分类器及其变体。主要任务是通过使用患者 SV 的图像将舌下静脉曲张分为轻度和重度。为了提高诊断准确性并加速训练过程，我们提出使用两种模型降维技术，即主成分分析结合切片逆回归和卷积神经网络（CNN），在数据预处理过程中提取有价值的特征。我们的结果表明，这两种提取方法可以减少 ML 方法的训练时间，Ridge-CNN 方法的准确率高达 87.5%，与经验丰富的中医医师相当。这种计算机辅助工具可以作为临床诊断的参考。此外，它可以供初级医生在临床环境中学习和使用。
