Tongue Diagnosis In Chinese Medicine Research Articles

Thermal radiation of tongue surface as a human computer interaction diagnostics technique based on image classification with software interface

Backgroundand Purpose: Thermal radiation plays a pivotal role in addressing recognition limitations encountered by traditional Chinese medicine's tongue diagnosis. Modern thermal radiation diagnostic instruments offer enhanced objectivity, and in this study, we strive to further improve the diagnostic process through the integration of Human-Computer Interaction (HCI) principles. Our objective is to introduce the thermal radiation DenseNet ensemble model, synergizing the strengths of Convolutional Neural Networks (CNNs) and HCI principles to advance tongue image recognition in healthcare. This approach aims to enhance classification accuracy, even when faced with limited training data. MethodologyTransfer learning, utilizing fine-tuned CNNs and DenseNet, is employed for precise classification, with a specific focus on distinguishing tooth-marked tongues. A portable hand-held thermal radiation diagnostic instrument, seamlessly integrated with HCI, is developed for both clinical use and research. Experimental resultsMeticulously curated datasets, formed through the annotation of thermal radiation images, confirm the superior performance of the DenseNet architecture in tooth mark and tongue feature recognition. The proposed tooth mark tongue recognition model exhibits a significant accuracy improvement of approximately 25% compared to similar tasks in existing studies. Additionally, an AI health detector tailored for Traditional Chinese Medicine (TCM) thermal radiation image recognition is introduced, showcasing the seamless integration of HCI principles into healthcare applications. This innovative tool not only assesses users' health but also provides personalized health recommendations, illustrating the transformative potential of combining technology and healthcare. Discussion and conclusionIn addressing the limitations of traditional tongue diagnosis, thermal radiation emerges as a key factor enhancing objectivity and accuracy. The thermal radiation DenseNet ensemble model, along with HCI integration through the portable hand-held device, proves effective for improved TCM thermal radiation image identification in healthcare. The fusion of technology and HCI principles underscores the potential to elevate health assessments and offer valuable guidance to users.

Progressively Multi-Scale Feature Fusion for Image Inpainting

The rapid advancement of Wise Information Technology of med (WITMED) has made the integration of traditional Chinese medicine tongue diagnosis and computer technology an increasingly significant area of research. The doctor obtains patient’s tongue images to make a further diagnosis. However, the tongue image may be broken during the process of collecting the tongue image. Due to the extremely complex texture of the tongue and significant individual differences, existing methods fail to fully obtain sufficient feature information, which result in inaccurate inpainted tongue images. To address this problem, we propose a recurrent tongue image inpainting algorithm based on multi-scale feature fusion called Multi-Scale Fusion Module and Recurrent Attention Mechanism Network (MSFM-RAM-Net). We first propose Multi-Scale Fusion Module (MSFM), which preserves the feature information of tongue images at different scales and enhances the consistency between structures. To simultaneously accelerate the inpainting process and enhance the quality of the inpainted results, Recurrent Attention Mechanism (RAM) is proposed. RAM focuses the network’s attention on important areas and uses known information to gradually inpaint image, which can avoid redundant feature information and the problem of texture confusion caused by large missing areas. Finally, we establish a tongue image dataset and use this dataset to qualitatively and quantitatively evaluate the MSFM-RAM-Net. The results shows that the MSFM-RAM-Net has a better effect on tongue image inpainting, with PSNR and SSIM increasing by 2.1% and 3.3%, respectively.

Automatic tongue image quality assessment using a multi-task deep learning model.

The quality of tongue images has a significant influence on the performance of tongue diagnosis in Chinese medicine. During the acquisition process, the quality of the tongue image is easily affected by factors such as the illumination, camera parameters, and tongue extension of the subject. To ensure that the quality of the collected images meet the diagnostic criteria of traditional Chinese Medicine practitioners, we propose a deep learning model to evaluate the quality of tongue images. First, we acquired the tongue images of the patients under different lighting conditions, exposures, and tongue extension conditions using the inspection instrument, and experienced Chinese physicians manually screened them into high-quality and unqualified tongue datasets. We then designed a multi-task deep learning network to classify and evaluate the quality of tongue images by adding tongue segmentation as an auxiliary task, as the two tasks are related and can promote each other. Finally, we adaptively designed different task weight coefficients of a multi-task network to obtain better tongue image quality assessment (IQA) performance, as the two tasks have relatively different contributions in the loss weighting scheme. Experimental results show that the proposed method is superior to the traditional deep learning tongue IQA method, and as an additional task of the network, it can output the tongue segmentation area, which provides convenience for follow-up clinical tongue diagnosis. In addition, we used network visualization to verify the effectiveness of the proposed method qualitatively.

Tooth-Marked Tongue Recognition Based on Mask Scoring R-CNN

Tooth-marked tongue is one of the important tongue features in Traditional Chinese Medicine tongue diagnosis. It reflects the dysfunction of liver, spleen and kidney, and contains rich pathological information. However, the recognition of tooth-marked tongue is challenging. Most existing methods only focus on the classification of tooth-marked tongue, and the exact location and number of teeth marks were not involved, which had no great practical significance for the subsequent treatment based on syndrome differentiation. In this paper, we try to solve these problems by proposing a method based on Mask Scoring R-CNN framework and transfer learning which can extract, visualize the teeth marks and identify the number of teeth marks. First, the tongue image is fed into residual network of depth 101 layers (ResNet-101) to generate regions of interest (RoIs) via region proposal network (RPN) and RoI feature via RoIAlign. Next, MaskIOU is then predicted using the predicted mask and RoI characteristics as inputs. The proposed approach achieved F1 score of 0.95. According to these experimental results, the approach can robustly detect and segment teeth marks, which can provide a basis for the severity analysis of tooth-marked tongue.

Pathological Change of Chronic Hepatitis B Patients with Different Tongue Coatings by Circular Multi-Omics Integrated Analysis

To compare the circular pathological changes of chronic hepatitis B (CHB) patients according to the tongue diagnosis. Totally 41 CHB patients with typical white tongue coating (WTC) or yellow tongue coating (YTC) were enrolled and 14 healthy volunteers with normal tongue manifestation served as controls. The mRNA expression of peripheral leukocytes was detected by GeneChips, and 9 genes were randomly selected for expression validation. Circular metabolites were detected by gas chromatographymass spectrometry. Biological information was analyzed based on ingenuity pathways analysis or metabolomics database and the integrated networks were constructed by ClueGO. A total of 945 and 716 differentially expressed genes were found in patients with WTC and YTC relative to healthy volunteers respectively. The biological information analysis indicated that CHB patients had obviously increased functions in cell death, apoptosis and necrosis (Z-score ⩾2, P<0.05) and decreased activation in T lymphocytes (Z-score ⩽-2, P<0.05), regardless of the tongue manifestation. Compared to patients with WTC, the YTC patients were predicted to be more active in functions related to virus replication (Z-score ⩾2, P<0.05), and the content of circular fatty acids, such as oleic acid (P=0.098) and lauric acid (P=0.035), and citric acid cycle-related metabolites were higher in the YTC patients (P<0.1). The integrated analysis based on differential genes and metabolites indicated that the most difference in the biological function network between the WTC and YTC patients was tumor necrosis factor receptor associated factor 6 mediated-nuclear factor kappa-B activation process. CHB patients with YTC had more severe inflammation and fatty acids metabolism aberrant than patients with WTC. The results facilitate the modern pathological annotation of Chinese medicine tongue diagnosis theory and provide a reference for the interpretation of pharmacological mechanisms of Chinese medicine treatment.

Study on Fat and Thin Analysis of Tongue Body Based on Two-Dimensional Ultrasonic Imaging

In the development of traditional Chinese medicine in China for five thousand years, the tongue clinic occupies a very important position. In Chinese medicine, it is an irreplaceable role in the diagnosis and treatment of Chinese medicine of important method. Tongue diagnosis as one of the most characteristic of Chinese medicine diagnosis has been from the previous subjective observation, relying on the experience to judge the era into the new era of tongue diagnosis objectification, quantification, and the introduction of computer digital image processing technology to more Accurate diagnosis. This article will mainly introduce the current in the Chinese and Western medicine on all aspects of the tongue diagnosis of the objective, micro, quantitative progress, the characteristics of tongue images and a variety of processing techniques, including tongue image automatic segmentation, and tongue fat and thin The Relationship and Modernization of Organ Pathology in Viscera. At the same time, a tongue-sized measurement method is introduced. Two-dimensional ultrasound imaging is used as a means of inspection to establish a set of ultrasonic tongue-sized measurement methods. The measured parameters include the two longitudinal planes of the tongue and the cross-section of the tongue. Out of the tongue size of the data, the overall analysis of the size of the tongue shape. This method combines some of the configuration line spacing, area, and circumference measurement functions to directly measure the data. Finally, through the accuracy test of the measurement method, it is proved that the measurement result of tongue two-dimensional ultrasound imaging is accurate. All the efforts of modernization are committed to the Chinese medicine tongue medicine to scientific can be quantified; Chinese medicine tongue diagnosis will also make a greater contribution to modern medicine.

Education of Chinese medicine tongue diagnosis by automatic tongue diagnosis system.

To investigate the efficacy of applying automatic tongue diagnosis system (ATDS) to the training of novice Chinese medicine (CM) doctors in the tongue diagnosis. A two-round experiment composed of pre- and after-training tests was performed. Two groups of CM doctors were formed by experienced doctors (ED) and novice doctors (ND). The intra- and inter-observer agreements for the ED and ND groups, and the inter-observer agreements between ATDS and ED, and ATDS and ND were derived first. After a 1-month training of the ND, the intra- and inter-observer agreements of the ND group, and the inter-observer agreement between ATDS and ND were obtained. The kappa value of agreement data for ND after training was significantly higher than ND before training with a level of P<0.05, indicating an obvious improvement of intra-observer agreement for ND after training with ATDS. The inter-observer agreements for the ED group, and ND group after training were significantly higher (P<0.05) than the ND group before training. In contrast, no significance is observed between the ED group and the ND group after training, indicating that physicians in the ND group achieve an inter-observer agreement to the same level as that of ED after 1-month training. This finding validates the utilization of ATDS as a mean of education in forging consensus during the learning process of tongue diagnosis.

Mysteries of the Tongue

Tongue Diagnosis is a diagnostic tool that is so important and pervasive in contemporary training and practice that it is usually considered and presented as if it had always been so. Diagrammatic and theoretical detail in Chinese medical textbooks give the impression that, as a system, tongue diagnosis has achieved a parity with pulse reading as a cornerstone of the contemporary clinical encounter. And yet, even a cursory look at a selection of classical texts, case histories and formularies from the Han to the Ming dynasty suggests that tongue diagnosis has not always enjoyed its current status. While the tongue is mentioned as an indicator of health or illness in classical texts, the privileging of the tongue in diagnosis is relatively recent. This article presents a preliminary survey of tongue diagnosis in Chinese medicine and offers some hypotheses about the process through which inspection of the tongue transformed from incidental observations to become a pillar of diagnosis. At the same time it is a practitioner̓s account and stands as a personal reflection on the value and relevance of engaging in history for practitioners of traditional medicines.