Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at an...Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.展开更多
Objective:To explore the value of tongue color combined with sublingual microcirculation in predicting the severity of mild to moderate acute cerebral infarction(ACI).Methods:From January to December 2022,90 patients ...Objective:To explore the value of tongue color combined with sublingual microcirculation in predicting the severity of mild to moderate acute cerebral infarction(ACI).Methods:From January to December 2022,90 patients with ACI were admitted to the Department of Neurology of Jiangsu Provincial Hospital of Integrated Traditional Chinese and Western Medicine.According to the NHISS score on the 5th day of admission,the patients were divided into mild group(35 cases)and moderate group(55 cases).The changes of tongue color and arterial lactate on the 5th day of admission were observed and monitored.Side-stream dark field imaging(SDF)was used to determine the total vascular density(TVD),perfused vascular density(PVD),perfused vascular ratio(PPV)and microvascular flow index(MFI).The multivariate logistic regression analysis was used to screen the risk factors for the severity of ACI,and the receiver operating characteristic curve(ROC)to evaluate their values in predicting ACI severity.Results:There was no significant difference in lactate between the two groups(P>0.05),and the frequency of red tongue in the mild group was higher,and the frequency of red tongue in the moderate group was significantly higher in the dark tongue group(P<0.05).The multivariate logistic regression analysis showed that PVD and PPV were independent risk factors for the severity of mild to moderate ACI(P<0.05).The ROC curve analysis showed that an area under the curve of 0.832 was achieved by the combination of PVD and PPV,which was larger than that of a single factor.Conclusion:Tongue color combined with sublingual microcirculation can be combined to predict the severity of mild to moderate ACI.展开更多
Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis,including tongue image segmentation and tongue color classification,improving their diagnostic accuracy.Methods LabelMe...Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis,including tongue image segmentation and tongue color classification,improving their diagnostic accuracy.Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results.A new dataset was constructed for tongue image segmentation.Tongue color was marked to build a classified dataset for network training.In this research,the Inception+Atrous Spatial Pyramid Pooling(ASPP)+UNet(IAUNet)method was proposed for tongue image segmentation,based on the existing UNet,Inception,and atrous convolution.Moreover,the Tongue Color Classification Net(TCCNet)was constructed with reference to ResNet,Inception,and Triple-Loss.Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification.IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+for tongue segmentation.TCCNet for tongue color classification was compared with VGG16 and GoogLeNet.Results IAUNet can accurately segment the tongue from original images.The results showed that the Mean Intersection over Union(MIoU)of IAUNet reached 96.30%,and its Mean Pixel Accuracy(MPA),mean Average Precision(mAP),F1-Score,G-Score,and Area Under Curve(AUC)reached 97.86%,99.18%,96.71%,96.82%,and 99.71%,respectively,suggesting IAUNet produced better segmentation than other methods,with fewer parameters.Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors.The experiment yielded ideal results,with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%,respectively.Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones.IAUNet can not only produce ideal tongue segmentation,but have better effects than those of PSPNet,SegNet,UNet,and DeepLabV3+,the traditional networks.As for tongue color classification,the proposed network,TCCNet,had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.展开更多
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 i...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.展开更多
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 me...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%.展开更多
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 pr...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.展开更多
Objective:To investigate color and microvascular blood flow of the tongue in the mini-swine with immune hepatic injury. Methods: Six Chinese mini-swine for experimental use, 3 males and 3 females, were randomly divide...Objective:To investigate color and microvascular blood flow of the tongue in the mini-swine with immune hepatic injury. Methods: Six Chinese mini-swine for experimental use, 3 males and 3 females, were randomly divided into two groups, normal group and model group, 3 swine in each group. The swine in the model group was administrated by injection of 5 mg/kg ConA into the vein of auricular back, once every other day, 3 times each week, for 2 weeks in total. The animal in the control group was administrated with equal volume of saline. At 9 o’clock in the morning of the 15th day of the experiment, each swine was anesthetized with intramuscular injection of 9 ml 2.5% pentobarbital sodium and 3 ml Maleate, and then picture of the tongue was taken, microvascular blood flow on the tongue and the liver was detected with a laser Doppler blood flowmeter; Blood was taken from the precaval vein. Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST),total bilirubin (Tbil) and total protein (TP) were determined; Pathological changes of the liver and tongue tissues were investigated by means of HE staining; Serum TNF-α content was detected with ELISA assay. Results: In the mini-swine with immune hepatic injury induced by ConA, the tongue color showed cyanotic color, microvascular perfusion in the liver and the tongue, and partial pressure of oxygen in the tongue tissue significantly decreased; and the microcirculatory perfusion of the tongue was significantly correlated with that of the liver and the HIS color spatial value of the tongue; Serum TNF-α content significantly increased. Conclusion: The mini-swine with immune hepatic injury induced by ConA conforms to pathological characteristics of immune hepatic injury. Formation of the cyanotic tongue is related with microcirculatory disturbance of the tongue, which can indirectly reflect hepatic microcirculatory state in the immune hepatic injury.展开更多
<b><span>Objectives:</span></b><span> Recently, an increasing number of elderly patients have complained of tongue pain and fissured tongue, while atrophy of the lingual papillae wit...<b><span>Objectives:</span></b><span> Recently, an increasing number of elderly patients have complained of tongue pain and fissured tongue, while atrophy of the lingual papillae with </span><span>low </span><span>nutrition has also become a commonly encountered condition. The viewpoint that lingual papilla atrophy reflects systemic and pathological conditions, such as diabetes and </span><span>in </span><span>the circulatory system abnormalities, is supported by many clinicians. The present study was conducted to clarify the relationship of degree of atrophy of tongue papillae with oral symptoms, with an aim to evaluate the usefulness of clinical diagnostic criteria for tongue papillae atrophy.</span><span> </span><b><span>Subjects and Methods: </span></b><span>The subjects were asked to protrude the tongue forward. The tongue was held at rest and impressions were taken of the tongue dorsum 15 mm posterior to the apex of the tongue. Surface impressions were measured using a Surfcoder SE300 and the obtained values were used to define surface roughness (Ra). Multivariate analyses of the relationships between the Ra measurements and the living environment, subjective symptoms of oral health, and survey results of measurements were performed to examine factors associated with Ra. The tongue was photographed with a digital camera, then RGB color value of four random points 15 mm from the tongue apex was determined. Using those findings, redness was calculated.</span><span> </span><b><span>Results and Conclusions:</span></b><span> One hundred and six subjects with a mean age of 79.5</span><span> </span><span>±</span><span> </span><span>9.2 years were analyzed. In the smooth group, there was more redness in the tongue dorsum as compared to the rough group. Total and Sagittal Ra values of subjects with dysphagia were lower than those without dysphagia, suggesting that the degree of oral mucosal atrophy </span><span>is</span><span> related to subjective symptoms of dysphagia. Subjects with high water intake had higher Coronal Ra values, which was considered to be related to the edematous state of the lingual papillae.</span>展开更多
Objective：To discuss the correlation of tongue manifestation with the site of cerebral infarction in patients with acute cerebral infarction.Methods：From March 2008 to February 2009,200 cases of hospitalized patient...Objective：To discuss the correlation of tongue manifestation with the site of cerebral infarction in patients with acute cerebral infarction.Methods：From March 2008 to February 2009,200 cases of hospitalized patients with first unilateral cerebral infarction were chosen in the Department of Neurology,Xuanwu Hospital.The correlation of different tongue color,fur texture,fur color with the site of cerebral Infarction was analyzed.Results：The site of cerebral infarction in patients were compared between different tongue color by Chisquare test（P=0.314）,and further correspondence analysis demonstrated that there was correlation between red tongue and cortical-subcortical infarction group.The site of cerebral infarction in patients were compared between thick fur group and thin fur group,cortical-subcortical infarction occurred more frequently in the former（P=0.0008）.The site of cerebral infarction in patients were compared between dry fur group,moist fur group and smooth fur group,correspondence analysis demonstrated there was correlation between dry fur and cortical-subcortical group.The site of cerebral infarction in the patients were compared between white fur group,white-yellow fur group and yellow fur group（P=0.010）,and correspondence analysis demonstrated there was correlation between white fur and brainstem infarction;white-yellow fur has relationship with cortical infarction;subcortical infarction was weakly related with white-yellow fur;there was closer relationship between yellow fur and cortical-subcortical infarction.Conclusion：The change of tongue manifestation was associated with the site of cerebral infarction in patients,providing a new combining site for diagnosing cerebrovascular diseases by integrative medicine.展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR11).
文摘Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.
文摘Objective:To explore the value of tongue color combined with sublingual microcirculation in predicting the severity of mild to moderate acute cerebral infarction(ACI).Methods:From January to December 2022,90 patients with ACI were admitted to the Department of Neurology of Jiangsu Provincial Hospital of Integrated Traditional Chinese and Western Medicine.According to the NHISS score on the 5th day of admission,the patients were divided into mild group(35 cases)and moderate group(55 cases).The changes of tongue color and arterial lactate on the 5th day of admission were observed and monitored.Side-stream dark field imaging(SDF)was used to determine the total vascular density(TVD),perfused vascular density(PVD),perfused vascular ratio(PPV)and microvascular flow index(MFI).The multivariate logistic regression analysis was used to screen the risk factors for the severity of ACI,and the receiver operating characteristic curve(ROC)to evaluate their values in predicting ACI severity.Results:There was no significant difference in lactate between the two groups(P>0.05),and the frequency of red tongue in the mild group was higher,and the frequency of red tongue in the moderate group was significantly higher in the dark tongue group(P<0.05).The multivariate logistic regression analysis showed that PVD and PPV were independent risk factors for the severity of mild to moderate ACI(P<0.05).The ROC curve analysis showed that an area under the curve of 0.832 was achieved by the combination of PVD and PPV,which was larger than that of a single factor.Conclusion:Tongue color combined with sublingual microcirculation can be combined to predict the severity of mild to moderate ACI.
基金Scientific Research Project of the Education Department of Hunan Province(20C1435)Open Fund Project for Computer Science and Technology of Hunan University of Chinese Medicine(2018JK05).
文摘Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis,including tongue image segmentation and tongue color classification,improving their diagnostic accuracy.Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results.A new dataset was constructed for tongue image segmentation.Tongue color was marked to build a classified dataset for network training.In this research,the Inception+Atrous Spatial Pyramid Pooling(ASPP)+UNet(IAUNet)method was proposed for tongue image segmentation,based on the existing UNet,Inception,and atrous convolution.Moreover,the Tongue Color Classification Net(TCCNet)was constructed with reference to ResNet,Inception,and Triple-Loss.Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification.IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+for tongue segmentation.TCCNet for tongue color classification was compared with VGG16 and GoogLeNet.Results IAUNet can accurately segment the tongue from original images.The results showed that the Mean Intersection over Union(MIoU)of IAUNet reached 96.30%,and its Mean Pixel Accuracy(MPA),mean Average Precision(mAP),F1-Score,G-Score,and Area Under Curve(AUC)reached 97.86%,99.18%,96.71%,96.82%,and 99.71%,respectively,suggesting IAUNet produced better segmentation than other methods,with fewer parameters.Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors.The experiment yielded ideal results,with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%,respectively.Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones.IAUNet can not only produce ideal tongue segmentation,but have better effects than those of PSPNet,SegNet,UNet,and DeepLabV3+,the traditional networks.As for tongue color classification,the proposed network,TCCNet,had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.
基金This Research was funded by the Deanship of Scientific Research at University of Business and Technology,Saudi Arabia.
文摘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.
文摘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%.
基金This paper was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,Saudi Arabia,under grant No.(D-79-305-1442).The authors,therefore,gratefully acknowledge DSR technical and financial support.
文摘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.
基金supported by a grant from Beijing Municipal Personnel and Organization Ministry (No.20071D0501800247)Natural Science Grant of Capital Medical University (No.2006ZR01)
文摘Objective:To investigate color and microvascular blood flow of the tongue in the mini-swine with immune hepatic injury. Methods: Six Chinese mini-swine for experimental use, 3 males and 3 females, were randomly divided into two groups, normal group and model group, 3 swine in each group. The swine in the model group was administrated by injection of 5 mg/kg ConA into the vein of auricular back, once every other day, 3 times each week, for 2 weeks in total. The animal in the control group was administrated with equal volume of saline. At 9 o’clock in the morning of the 15th day of the experiment, each swine was anesthetized with intramuscular injection of 9 ml 2.5% pentobarbital sodium and 3 ml Maleate, and then picture of the tongue was taken, microvascular blood flow on the tongue and the liver was detected with a laser Doppler blood flowmeter; Blood was taken from the precaval vein. Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST),total bilirubin (Tbil) and total protein (TP) were determined; Pathological changes of the liver and tongue tissues were investigated by means of HE staining; Serum TNF-α content was detected with ELISA assay. Results: In the mini-swine with immune hepatic injury induced by ConA, the tongue color showed cyanotic color, microvascular perfusion in the liver and the tongue, and partial pressure of oxygen in the tongue tissue significantly decreased; and the microcirculatory perfusion of the tongue was significantly correlated with that of the liver and the HIS color spatial value of the tongue; Serum TNF-α content significantly increased. Conclusion: The mini-swine with immune hepatic injury induced by ConA conforms to pathological characteristics of immune hepatic injury. Formation of the cyanotic tongue is related with microcirculatory disturbance of the tongue, which can indirectly reflect hepatic microcirculatory state in the immune hepatic injury.
文摘<b><span>Objectives:</span></b><span> Recently, an increasing number of elderly patients have complained of tongue pain and fissured tongue, while atrophy of the lingual papillae with </span><span>low </span><span>nutrition has also become a commonly encountered condition. The viewpoint that lingual papilla atrophy reflects systemic and pathological conditions, such as diabetes and </span><span>in </span><span>the circulatory system abnormalities, is supported by many clinicians. The present study was conducted to clarify the relationship of degree of atrophy of tongue papillae with oral symptoms, with an aim to evaluate the usefulness of clinical diagnostic criteria for tongue papillae atrophy.</span><span> </span><b><span>Subjects and Methods: </span></b><span>The subjects were asked to protrude the tongue forward. The tongue was held at rest and impressions were taken of the tongue dorsum 15 mm posterior to the apex of the tongue. Surface impressions were measured using a Surfcoder SE300 and the obtained values were used to define surface roughness (Ra). Multivariate analyses of the relationships between the Ra measurements and the living environment, subjective symptoms of oral health, and survey results of measurements were performed to examine factors associated with Ra. The tongue was photographed with a digital camera, then RGB color value of four random points 15 mm from the tongue apex was determined. Using those findings, redness was calculated.</span><span> </span><b><span>Results and Conclusions:</span></b><span> One hundred and six subjects with a mean age of 79.5</span><span> </span><span>±</span><span> </span><span>9.2 years were analyzed. In the smooth group, there was more redness in the tongue dorsum as compared to the rough group. Total and Sagittal Ra values of subjects with dysphagia were lower than those without dysphagia, suggesting that the degree of oral mucosal atrophy </span><span>is</span><span> related to subjective symptoms of dysphagia. Subjects with high water intake had higher Coronal Ra values, which was considered to be related to the edematous state of the lingual papillae.</span>
基金Supported by the Projects of Capital Medical Technology Development Foundation(No.2005-SF-II-038)the Projects of Beijing Administration of Traditional Chinese Medicine(No.JJ2007-035 and JZZ-Ⅵ26)
文摘Objective：To discuss the correlation of tongue manifestation with the site of cerebral infarction in patients with acute cerebral infarction.Methods：From March 2008 to February 2009,200 cases of hospitalized patients with first unilateral cerebral infarction were chosen in the Department of Neurology,Xuanwu Hospital.The correlation of different tongue color,fur texture,fur color with the site of cerebral Infarction was analyzed.Results：The site of cerebral infarction in patients were compared between different tongue color by Chisquare test（P=0.314）,and further correspondence analysis demonstrated that there was correlation between red tongue and cortical-subcortical infarction group.The site of cerebral infarction in patients were compared between thick fur group and thin fur group,cortical-subcortical infarction occurred more frequently in the former（P=0.0008）.The site of cerebral infarction in patients were compared between dry fur group,moist fur group and smooth fur group,correspondence analysis demonstrated there was correlation between dry fur and cortical-subcortical group.The site of cerebral infarction in the patients were compared between white fur group,white-yellow fur group and yellow fur group（P=0.010）,and correspondence analysis demonstrated there was correlation between white fur and brainstem infarction;white-yellow fur has relationship with cortical infarction;subcortical infarction was weakly related with white-yellow fur;there was closer relationship between yellow fur and cortical-subcortical infarction.Conclusion：The change of tongue manifestation was associated with the site of cerebral infarction in patients,providing a new combining site for diagnosing cerebrovascular diseases by integrative medicine.
