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 (mPA) of 73.00%, an intersection over union (IoU) of 60.00%, and a mean intersection over union (mIoU) 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.

OBJECTIVE: To observe the effects of a traditional Chinese medicine (TCM) capsule for replenishing qi, nourishing yin and activating blood on Notch/Hes1 signaling pathway in the renal tissue and vascular endothelial CD34 and CD144 expressions in a rat model of diabetic nephropathy. METHODS: Rat models of early-stage diabetic nephropathy were established by left nephrectomy and high- fat and high- sugar feeding combined with intraperitoneal injection of STZ. The rats were randomized into model group, benazepril group, and high-, moderate-, and low-dose TCM capsule groups for corresponding treatments, with 6 normal rats as the control group. After 8 weeks of drug treatment, blood glucose and 24-h urinary albumin of the rats were measured, and the renal histopathology was observed with HE staining; Hes1 expression in the renal tissue was detected with immunohistochemical staining, and the renal expressions of CD34 and CD144 were detected using Western blotting. RESULTS: Compared with the normal control group, the rat models of diabetic nephropathy showed obvious abnormalities in 24- h urinary albumin and expressions of Hes1, CD34 and CD144d. The TCM capsule at both the high and moderate doses significantly reduced 24-h urinary albumin in the rats; the renal expressions of Hes1 and CD34 was significantly reduced in all the dose groups, and the expression of CD144 was significantly reduced in the high- dose group. Compared with benazepril group, the TCM capsule obviously reduced CD34 expression at all the 3 doses and lowered CD144 expression at the low dose. Histopathologically, the rats in the model group showed glomerular hypertrophy, increased mesenteric matrix, thickening and widening of the mesenteric membrane, and nodular hyperplasia. These pathologies were obviously alleviated by treatment with the TCM capsule at the high and moderate doses. CONCLUSIONS The Traditional Chinese medicine (TCM) capsule for replenishing qi, nourishing yin and activating blood can reduce Hes1, CD34 and CD144 in kidney tissue of model rats, play a protective role on kidney function and delay the development of DN.
Animals ; Diabetic Nephropathies ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional ; Qi ; Rats ; Signal Transduction ; Transcription Factor HES-1