Simulated Annealing with Deep Learning Based Tongue Image Analysis for Heart Disease Diagnosis

Tongue image analysis is an efficient and non-invasive technique to determine the internal organ condition of a patient in oriental medicine,for example,traditional Chinese medicine(TCM),Japanese traditional herbal medicine,and traditional Korean medicine(TKM).The diagnosis procedure is mainly based on the expert’s knowledge depending upon the visual inspec-tion comprising color,substance,coating,form,and motion of the tongue.But conventional tongue diagnosis has limitations since the procedure is inconsistent and subjective.Therefore,computer-aided tongue analyses have a greater potential to present objective and more consistent health assess-ments.This manuscript introduces a novel Simulated Annealing with Transfer Learning based Tongue Image Analysis for Disease Diagnosis(SADTL-TIADD)model.The presented SADTL-TIADD model initially pre-processes the tongue image to improve the quality.Next,the presented SADTL-TIADD technique employed an EfficientNet-based feature extractor to generate useful feature vectors.In turn,the SA with the ELM model enhances classification efficiency for disease detection and classification.The design of SA-based parameter tuning for heart disease diagnosis shows the novelty of the work.A wide-ranging set of simulations was performed to ensure the improved performance of the SADTL-TIADD algorithm.The experimental outcomes highlighted the superior of the presented SADTL-TIADD system over the compared methods with maximum accuracy of 99.30%.

Advances in Tongue Diagnosis Objectification of Traditional Chinese Medicine

Tongue diagnosis is a non-invasive,efficient,and accurate method for determining a person’s physical condition,and plays an essential role in disease diagnosis and health management.However,tongue diagnosis is easily influenced by the subjective experience of the practitioner and the light environment.In addition,tongue diagnosis lacks clear quantitative indicators and objective records.This all limits the transmission and development of tongue diagnosis.Therefore,the acquisition and analysis of tongue information using image equipment,image processing and computer vision have become a hot research topic for the objectification of tongue diagnosis.This paper reviews the research progress of tongue diagnosis objectification in Traditional Chinese medicine.The tongue image acquisition,color correction,segmentation,feature extraction and analysis,and disease prediction included in the study of tongue diagnosis objectification are reviewed.The shortcomings of current automated tongue diagnosis systems and future research ideas are also summarized to provide a reference for further development of tongue diagnosis objectification.

Tongue image feature correlation analysis in benign lung nodules and lung cancer

Objective To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer.Methods From July 1;2020 to March 31;2022;clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hos-pital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Ex-amination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chi-nese Medicine;respectively.We obtained tongue images from patients with benign lung nod-ules and lung cancer using the TFDA-1 digital tongue diagnosis instrument;and analyzed these images with the TDAS V2.0 software.The extracted indicators included color space pa-rameters in the Lab system for both the tongue body(TB)and tongue coating(TC)(TB/TC-L;TB/TC-a;and TB/TC-b);textural parameters[TB/TC-contrast(CON);TB/TC-angular second moment(ASM);TB/TC-entropy(ENT);and TB/TC-MEAN];as well as TC parameters(perAll and perPart).The bivariate correlation of TB and TC features was analyzed using Pearson’s or Spearman’s correlation analysis;and the overall correlation was analyzed using canonical correlation analysis(CCA).Results Samples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values.Simple correlation analysis indi-cated that the correlation of TB-L with TC-L;TB-b with TC-b;and TB-b with perAll in lung cancer group was higher than that in benign nodules group.Moreover;the correlation of TB-a with TC-a;TB-a with perAll;and the texture parameters of the TB(TB-CON;TB-ASM;TB-ENT;and TB-MEAN)with the texture parameters of the TC(TC-CON;TC-ASM;TC-ENT;and TC-MEAN)in benign nodules group was higher than lung cancer group.CCA further demon-strated a strong correlation between the TB and TC parameters in lung cancer group;with the first and second pairs of typical variables in benign nodules and lung cancer groups indicat-ing correlation coefficients of 0.918 and 0.817(P<0.05);and 0.940 and 0.822(P<0.05);re-spectively.Conclusion Benign lung nodules and lung cancer patients exhibited differences in correla-tion in the L;a;and b values of the TB and TC;as well as the perAll value of the TC;and the texture parameters(TB/TC-CON;TB/TC-ASM;TB/TC-ENT;and TB/TC-MEAN)between the TB and TC.Additionally;there were differences in the overall correlation of the TB and TC be-tween the two groups.Objective tongue diagnosis indicators can effectively assist in the diag-nosis of benign lung nodules and lung cancer;thereby providing a scientific basis for the ear-ly detection;diagnosis;and treatment of lung cancer.

Deep learning-based recognition of stained tongue coating images

Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis.Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously.Traditionally,physicians examine the characteristics of tongue prior to decision-making.In this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed.This model can reduce the physical harm made to the patients.Several tongue image analytical methodologies have been proposed earlier.However,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis model.With this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI model.The proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal process.Besides,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization(OGSO)algorithm.At last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color images.The design of OGSO algorithm for SqueezeNet model shows the novelty of the work.To assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several measures.The resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

Application of artificial intelligence in tongue diagnosis of traditional Chinese medicine:A review

Tongue diagnosis is an important process to non-invasively assess the condition of a patient’s internal organs in traditional Chinese medicine(TCM)and each part of the tongue is related to corresponding internal organs.Due to continuing computer technological advances,especially the artificial intelligence(AI)methods have achieved significant success in tackling tongue image acquisition,processing,and classification,novel AI methods are being introduced in traditional Chinese medicine tongue diagnosis medical practices.Traditional tongue diagnose depends on observations of tongue characteristics,such as color,shape,texture,moisture,etc.by traditional Chinese medicine physicians.The appearance of the tongue color,texture and coating reflects the improvement or deterioration of patient’s conditions.Moreover,AI can now distinguish patient’s condition through tongue images,texture or coating,which is all possible increasingly with help from traditional Chinese medicine physicians under the traditional Chinese medicine tongue theory.AI has enabled humans to do what was previously unimagined:traditional Chinese medicine tongue diagnosis with feeding a large amount of tongue image and tongue texture/coating data to train the AI modes.This review focuses on the research advances of AI in TCM tongue diagnosis thus far to identify the major scientific methods and prospects.In this article,we tried to review the AI application in resolving the tongue diagnosis of traditional Chinese medicine on color correction,tongue image extraction,tongue texture/coating segmentation.

Research Progress of TCM Tongue Diagnosis in Spleen and Stomach Disease

According to the theory of traditional Chinese medicine,the tongue is thought to be an outer manifestation of the status of the viscera.It can be divided to spleen-stomach area,liver-gall area,kidney area,and heart-lung area.The tongue coating is formed by stomach-Qi.the tongue,especially spleen-stomach area,and the tongue coating may reflect the status of the spleen and stomach.This article summarizes the research overview of TCM tongue diagnosis in spleen and stomach disease.

Altered oral microbiota in chronic hepatitis B patients with different tongue coatings

AIM To elucidate tongue coating microbiota and metabolic differences in chronic hepatitis B(CHB) patients with yellow or white tongue coatings.METHODS Tongue coating samples were collected from 53 CHBpatients(28 CHB yellow tongue coating patients and 25 CHB white tongue coating patients) and 22 healthy controls.Microbial DNA was extracted from the tongue samples,and the bacterial 16 S ribosomal RNA gene V3 region was amplified from all samples and sequenced with the Ion Torrent PGM^(TM)sequencing platform according to the standard protocols.The metabolites in the tongue coatings were evaluated using a liquid chromatographymass spectrometry(LC-MS) platform.Statistical analyses were then performed.RESULTS The relative compositions of the tongue coating microbiotas and metabolites in the CHB patients were significantly different from those of the healthy controls,but the tongue coating microbiota abundances and diversity levels were not significantly different.Compared with the CHB white tongue coating patients,the CHB yellow tongue coating patients had higher hepatitis B viral DNA(HBV-DNA) titers(median 21210 vs 500,respectively,P = 0.03) and a significantly lower level of Bacteroidetes(20.14% vs 27.93%,respectively,P = 0.013) and higher level of Proteobacteria(25.99% vs 18.17%,respectively,P = 0.045) in the microbial compositions at the phylum level.The inferred metagenomic pathways enriched in the CHB yellow tongue coating patients were mainly those involved in amino acid metabolism,which was consistent with the metabolic disorder.The abundances of bacteria from Bacteroidales at the order level were higher in the CHB white tongue coating patients(19.2% vs 27.22%,respectively,P = 0.011),whereas Neisseriales were enriched in the yellow tongue coating patients(21.85% vs 13.83%,respectively,P = 0.029).At the family level,the abundance of Neisseriaceae in the yellow tongue patients was positively correlated with the HBV-DNA level but negatively correlated with the S-adenosyl-L-methionine level.CONCLUSION This research illustrates specific clinical features and bacterial structures in CHB patients with different tongue coatings,which facilitates understanding of the traditional tongue diagnosis.

Establishing and validating a spotted tongue recognition and extraction model based on multiscale convolutional neural network

Objective In tongue diagnosis,the location,color,and distribution of spots can be used to speculate on the viscera and severity of the heat evil.This work focuses on the image analysis method of artificial intelligence(AI)to study the spotted tongue recognition of traditional Chinese medicine(TCM).Methods A model of spotted tongue recognition and extraction is designed,which is based on the principle of image deep learning and instance segmentation.This model includes multiscale feature map generation,region proposal searching,and target region recognition.Firstly,deep convolution network is used to build multiscale low-and high-abstraction feature maps after which,target candidate box generation algorithm and selection strategy are used to select high-quality target candidate regions.Finally,classification network is used for classifying target regions and calculating target region pixels.As a result,the region segmentation of spotted tongue is obtained.Under non-standard illumination conditions,various tongue images were taken by mobile phones,and experiments were conducted.Results The spotted tongue recognition achieved an area under curve(AUC)of 92.40%,an accuracy of 84.30%with a sensitivity of 88.20%,a specificity of 94.19%,a recall of 88.20%,a regional pixel accuracy index pixel accuracy(PA)of 73.00%,a mean pixel accuracy(m PA)of73.00%,an intersection over union(Io U)of 60.00%,and a mean intersection over union(mIo U)of 56.00%.Conclusion The results of the study verify that the model is suitable for the application of the TCM tongue diagnosis system.Spotted tongue recognition via multiscale convolutional neural network(CNN)would help to improve spot classification and the accurate extraction of pixels of spot area as well as provide a practical method for intelligent tongue diagnosis of TCM.

Recent Progresses in Analysis of Tongue Manifestation for Traditional Chinese Medicine

Tongue diagnosis is one of the most precious and widely used diagnostic methods in Traditional Chinese Medicine (TCM). However, due to its subjective, qualitative and experience-dependent nature, the studies on tongue characterization have been widely emphasized. This paper surveys recent progresses in analysis of tongue manifestation. These new developments include the cross-network and cross-media color reproduction of tongue image, the automatic segmentation of tongue body based on knowledge, the automatic analysis of curdiness and griminess for the tongue fur and the automatic analysis of plumpness, wryness and dot -thorn of tongue body. The clinic experiments verify the validity of these new methods.

Fusion Based Tongue Color Image Analysis Model for Biomedical Applications

Tongue diagnosis is a novel and non-invasive approach commonly employed to carry out the supplementary diagnosis over the globe.Recently,several deep learning(DL)based tongue color image analysis models have existed in the literature for the effective detection of diseases.This paper presents a fusion of handcrafted with deep features based tongue color image analysis(FHDF-TCIA)technique to biomedical applications.The proposed FDHF-TCIA technique aims to investigate the tongue images using fusion model,and thereby determines the existence of disease.Primarily,the FHDF-TCIA technique comprises Gaussian filtering based preprocessing to eradicate the noise.The proposed FHDF-TCIA model encompasses a fusion of handcrafted local binary patterns(LBP)withMobileNet based deep features for the generation of optimal feature vectors.In addition,the political optimizer based quantum neural network(PO-QNN)based classification technique has been utilized for determining the proper class labels for it.A detailed simulation outcomes analysis of the FHDF-TCIA technique reported the higher accuracy of 0.992.

Foreign body granuloma in the tongue differentiated from tongue cancer:A case report

BACKGROUND Embedded foreign bodies in the tongue are rarely seen in clinical settings.An untreated foreign body can cause a granuloma which often presents as an enlarged tongue mass.However,if foreign body ingestion status is unknown,physical examination and magnetic resonance imaging(MRI)tend to lead to suspicion of tongue cancer,especially in older patients.Thus,differential diagnosis of an enlarged tongue mass is important,especially because it is closely related to the choice of treatment method.CASE SUMMARY A 61-year-old woman was admitted to the hospital with pain and noticeable swelling in the tongue that had persisted for over 1 mo.She had no previous medical history.MRI revealed abnormal signal intensities that were indicative of a neoplasm.Thus,the oral surgeon and radiologist arrived at a primary diagnosis of tongue cancer.The patient visited the Ear Nose and Throat Department for further consultation and underwent an ultrasound examination of the tongue.The ultrasonography was consistent with a linear hyperechoic foreign body which was indicative of an embedded foreign body(bone)in the tongue,even though the patient denied any history of foreign body ingestion.Complete surgical enucleation of the lesion was conducted.The mass which included a fish bone was completely removed.The post-operative pathological examination confirmed that the mass was a granuloma containing collagen fibers,macrophages and chronic inflammatory cells.The patient recovered without complications over a 2 mo follow-up period.CONCLUSION We report a rare case of foreign body granuloma in the tongue that was primarily diagnosed as tongue cancer.The MRI and ultrasound examinations revealed a piece of bone in the left lateral aspect of the tongue.The granuloma,which contained a fish bone,was completely removed via surgery and confirmed via biopsy.Differential diagnosis of the enlarged tongue mass was critical to the selection of treatment method.

Expression of Plectin-1 and Trichohyalin in Human Tongue Cancer Cells

In basal squamous cells, plectin-1 interacts with intermediate filaments, whereas trichohyalin, which is distributed primarily in the medulla and inner root sheath cells of human hair follicles, plays a role in strengthening cells during keratinization. Although both cytoskeletal proteins occur in trace amounts in human tongue epithelial cells, there are minimal data on their expression in human tongue primary cancer cells. We therefore investigated the expression of plectin-1 and trichohyalin in human tongue epithelial cell line (DOK) and tongue cancer cell line (BICR31) using western blotting and FITC-labeled immunocytochemistry techniques. DOK and BICR31 cells were cultivated to subconfluence in Dulbecco’s Modified Eagle’s Medium containing 0.4 μg/ml of hydrocortisone and 10% fetal bovine serum, and the levels of trichohyalin and plectin-1 were determined by western blot analysis and immunocytochemical staining. Trichohyalin expression was clearly observed, with no differences between DOK and BICR31 cells. Although DOK cells expressed trace levels of plectin-1, obvious plectin-1 bands were detected in western blot analyses of BICR31 cells. Immunocytochemical staining revealed that trichohyalin and plectin-1 localize in the cytoplasm. Trichohyalin was diffusely distributed in both cell lines, and colocalization of trichohyalin and cytokeratin 1/10 was observed in almost all BICR31 cells. There were no correlations between western blot and immunocytochemical data for trichohyalin. Conversely, correlations in immunochemical reactions for plectin-1 were observed. Most DOK cells showed no localization of plectin-1, but strong reactions were detected in the cytoplasm of BICR31 cells. These results indicate that trichohyalin is expressed by cancerous tongue epithelial cells during various stages of malignancy and that plectin-1 provides an index of malignancy.

Template Guided Live Wire and Its Application on Automatic Extraction of Tongue in Digital Image

In this paper, we propose a novel automatic object extraction algorithm, named the Template Guided Live Wire, based on the popularly used live-wire techniques. We discuss in details the novel method’s applications on tongue extraction in digital images. With the guides of a given template curve which approximates the tongue’s shape, our method can finish the extraction of tongue without any human intervention. In the paper, we also discussed in details how the template guides the live wire, and why our method functions more effectively than other boundary based segmentation methods especially the snake algorithm. Experimental results on some tongue images are as well provided to show our method’s better accuracy and robustness than the snake algorithm.

Tongue diagnosis based on hue-saturation value color space: controlled study of tongue appearance in patients treated with percutaneous coronary intervention for coronary heart disease

Objective To analyze the characteristics of tongue imaging color parameters in patients treated with percutaneous coronary intervention(PCI)and non-PCI for coronary atherosclerotic heart disease(CHD),and to observethe effects of PCI on the tongue images of patients as a basis for the clinical diagnosis and treatment of patientswith CHD.Methods This study used a retrospective cross-sectional survey to analyze tongue photographs and medicalhistory information from 204 patients with CHD between November 2018 and July 2020.Tongue images ofeach subject were obtained using the Z-BOX Series traditional Chinese medicine(TCM)intelligent diagnosisinstruments,the SMX System 2.0 was used to transform the image data into parameters in the HSV color space,and finally the parameters of the tongue image between patients in the PCI-treated and non-PCI-treated groupsfor CHD were analyzed.Results Among the 204 patients,112 were in the non-PCI treatment group(38 men and 74 women;average age of(68.76±9.49)years),92 were in the PCI treatment group(66 men and 26 women;average age of(66.02±10.22)years).In the PCI treatment group,the H values of the middle and tip of the tongue and the overall coating of thetongue were lower(P<0.05),while the V values of the middle,tip,both sides of the tongue,the whole tongueand the overall coating of the tongue were higher(P<0.05).Conclusion The color parameters of the tongue image could reflect the physical state of patients treated withPCI,which may provide a basis for the clinical diagnosis and treatment of patients with CHD.

舌诊在慢性肝病中的应用刍议

舌诊作为中医特色诊法之一,在肝病的诊治与预防中具有重要意义。慢性肝病患者在不同的病理阶段有不同的舌象改变。舌色可反映肝病轻重缓急,如缓解期多见淡红舌,发作期多见红绛舌,而严重肝病多见青紫舌;舌形可反映肝病形气神,其中长条舌、圆柱舌是慢性肝病的常见舌形,舌缘肿大或者肝积舌多代表慢性肝病中的占位性病变;舌苔与肝病轻重及疾病预后有关,还可辅助诊断慢性肝病的细菌感染情况。此外,舌象在慢性肝病的临床用药指导中也具有重要意义,可多方面、多角度指导临床。

基于机器学习的舌象形质诊断分析研究现状与展望

基于图像的智能化诊断是中医舌诊现代化研究的重要方向。近年来,以卷积神经网络(CNNs)、Transformers等深度学习为代表的机器学习方法被广泛应用于电子计算机断层扫描(CT)、核磁共振成像(MRI)等医学影像图像分析领域,使得临床决策更加高效和精准。先进的人工智能技术也为中医舌诊医疗器械研发和数字化中医舌诊方法创造了新的机遇,促进了中医舌诊的标准化和智能化。经典图像分析方法实现了舌象的颜色数据化表达,但对于复杂的舌象形质特征如齿痕、点刺、裂纹、厚薄、腐腻、剥苔等的综合识别分析仍是当前舌诊研究面临的瓶颈问题。本文从舌象形质特征的智能分析与病证诊断应用等方面展开论述,归纳了经典的图像分析方法与深度学习方法的研究现状,梳理了舌象特征在临床疾病风险预测中的应用情况,提出了人工智能舌诊技术的机遇挑战和发展方向。总之,传统中医舌诊与人工智能技术结合,将有效提升中医舌诊的科学内涵,提升舌诊临床普适应用,推动中医诊疗模式的现代化发展。

许家松辨治慢性肾病舌诊经验

介绍许家松教授运用温病学舌诊理论,结合“辨证论治五步法”辨治慢性肾病的经验。许师从“整体恒动观”的中医学指导思想出发,以温病学辨舌法中三焦脏腑定位、正邪关系等内容为理论基础,以方药中先生“辨证论治五步法”为临床诊疗思路,认为舌象的观察要以整体到局部为顺序,重视舌象在治疗前后的对比,总结舌象动态变化,将舌象作为慢性肾病中医临床上辨证辨病、诊疗效果、判断预后的重要客观证据之一,并重视其在治则治法选择上起到的关键作用。

《温疫论》《温热论》外感病中疫病舌象特点

此文通过对《温疫论》《温热论》的研究,以文献分析的方法对外感病疫病舌诊特点进行探析。提出在疫病舌诊中,以舌苔为重的观点。在疫病中,观苔可知感邪轻重,舌苔对邪气的传变感应迅速;强调白苔不尽为寒的思想,在疫病中不能尽断白苔为寒;总结出疫病舌象对病理产物有预示作用,舌象可参与辨证,可在临床上定方论药。对疫病舌诊特点进行探析,不仅可以对临床新发疫病有参考价值,在外感病中,也可以进行鉴别使用。

舌诊客观化在中医药临床诊断和疗效评价中应用与价值

舌诊是中医四诊之一,在临床诊疗中具有重要的应用价值。随着新技术手段的发展应用,中医舌诊客观化研究不断深入,相关研究成果在实践中得到应用。文章通过梳理近年的中医舌诊客观化研究成果,重点围绕基于图像处理技术和分子生物学技术的中医舌诊客观化研究,分析目前舌诊客观化在中医药临床疾病诊断和疗效评价中的价值和存在问题,为相关研究提供参考。