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 intelligenc...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.展开更多
Objective To analyze the relationship between Helicobacter pylori(H.pylori)infection and the manifestations of the tongue.Methods Without language restrictions,we searched databases including CAJD,CBMdisc,CDFD,CMFD,Pu...Objective To analyze the relationship between Helicobacter pylori(H.pylori)infection and the manifestations of the tongue.Methods Without language restrictions,we searched databases including CAJD,CBMdisc,CDFD,CMFD,PubMed,Cochrane,and EMBASE and made meta-analysis for all literature we have collected.Results Six studies with 975 patients and 50 healthy people in total were included in the analytic pool.There were significant differences in the numbers of H.pylori infections between patients with thin and thick tongue coatings(OR=2.02,95%CI 1.32–3.07,P=0.001).In yellow tongue subgroup,there was a significant difference between patients with thick and thin coatings(OR=2.09,95%CI 1.16–3.77,P=0.01).There was a significant difference in the prevalence of H.pylori infection between patients with yellow and white coatings(OR=2.86,95%CI 2.10–3.90,P<0.001).The frequency of H.pylori infection was significantly increased in patients with red and purple tongues compared with those with pale red tongues(OR=3.42,95%CI 2.40–4.88,P<0.001;OR=7.51,95%CI 3.57–15.79,P<0.001).Conclusion Red or purple tongues and yellow tongues with thick coatings are indicators of a risk of H.pylori infection in patients with GI symptoms.Our study shows that tongue manifestations could serve as a feasible predictor of H.pylori infection in patients with GI symptoms,and determination of the exact association between tongue manifestations and H.pylori infection could enable an understanding of the objectivity of TCM.Long-term and rigorous controlled trials are needed in the future to evaluate the correlating factors.展开更多
基金Anhui Province College Natural Science Fund Key Project of China(KJ2020ZD77)the Project of Education Department of Anhui Province(KJ2020A0379)。
文摘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.
基金funding support from the National Natural Science Foundation of China (No.81503627)Hunan Provincial Innovation Platform Open Fund (No.14K072)+1 种基金the Education Department of Hunan Province Project (No.15C1045)the State Key Subject of TCM diagnostics in Hunan University of Chinese Medicine (No.2015ZYZD27)
文摘Objective To analyze the relationship between Helicobacter pylori(H.pylori)infection and the manifestations of the tongue.Methods Without language restrictions,we searched databases including CAJD,CBMdisc,CDFD,CMFD,PubMed,Cochrane,and EMBASE and made meta-analysis for all literature we have collected.Results Six studies with 975 patients and 50 healthy people in total were included in the analytic pool.There were significant differences in the numbers of H.pylori infections between patients with thin and thick tongue coatings(OR=2.02,95%CI 1.32–3.07,P=0.001).In yellow tongue subgroup,there was a significant difference between patients with thick and thin coatings(OR=2.09,95%CI 1.16–3.77,P=0.01).There was a significant difference in the prevalence of H.pylori infection between patients with yellow and white coatings(OR=2.86,95%CI 2.10–3.90,P<0.001).The frequency of H.pylori infection was significantly increased in patients with red and purple tongues compared with those with pale red tongues(OR=3.42,95%CI 2.40–4.88,P<0.001;OR=7.51,95%CI 3.57–15.79,P<0.001).Conclusion Red or purple tongues and yellow tongues with thick coatings are indicators of a risk of H.pylori infection in patients with GI symptoms.Our study shows that tongue manifestations could serve as a feasible predictor of H.pylori infection in patients with GI symptoms,and determination of the exact association between tongue manifestations and H.pylori infection could enable an understanding of the objectivity of TCM.Long-term and rigorous controlled trials are needed in the future to evaluate the correlating factors.
