A multi-step approach for tongue image classification in patients with diabetes.

BACKGROUND: In China, diabetes is a common, high-incidence chronic disease. Diabetes has become a severe public health problem. However, the current diagnosis and treatment methods are difficult to control the progress of diabetes. Traditional Chinese Medicine (TCM) has become an option for the treatment of diabetes due to its low cost, good curative effect, and good accessibility. OBJECTIVE: Based on the tongue images data to realize the fine classification of the diabetic population, provide a diagnostic basis for the formulation of individualized treatment plans for diabetes, ensure the accuracy and consistency of the TCM diagnosis, and promote the objective and standardized development of TCM diagnosis. METHODS: We use the TFDA-1 tongue examination instrument to collect the tongue images of the subjects. Tongue Diagnosis Analysis System (TDAS) is used to extract the TDAS features of the tongue images. Vector Quantized Variational Autoencoder (VQ-VAE) extracts VQ-VAE features from tongue images. Based on VQ-VAE features, K-means clustering tongue images. TDAS features are used to describe the differences between clusters. Vision Transformer (ViT) combined with Grad-weighted Class Activation Mapping (Grad-CAM) is used to verify the clustering results and calculate positioning diagnostic information. RESULTS: Based on VQ-VAE features, K-means divides the diabetic population into 4 clusters with clear boundaries. The silhouette, calinski harabasz, and davies bouldin scores are 0.391, 673.256, and 0.809, respectively. Cluster 1 had the highest Tongue Body L (TB-L) and Tongue Coating L (TC-L) and the lowest Tongue Coating Angular second moment (TC-ASM), with a pale red tongue and white coating. Cluster 2 had the highest TC-b with a yellow tongue coating. Cluster 3 had the highest TB-a with a red tongue. Group 4 had the lowest TB-L, TC-L, and TB-b and the highest Per-all with a purple tongue and the largest tongue coating area. ViT verifies the clustering results of K-means, the highest Top-1 Classification Accuracy (CA) is 87.8%, and the average CA is 84.4%. CONCLUSIONS: The study organically combined unsupervised learning, self-supervised learning, and supervised learning and designed a complete diabetic tongue image classification method. This method does not rely on human intervention, makes decisions based entirely on tongue image data, and achieves state-of-the-art results. Our research will help TCM deeply participate in the individualized treatment of diabetes and provide new ideas for promoting the standardization of TCM diagnosis.

Intelligent Diabetes Detection System based on Tongue Datasets.

BACKGROUND: Scanning Electron Microscope (SEM) Camera Imaging shows and helps analyze hidden organs in the human body. SEM image analysis provides in-depth and critical details of organ abnormalities. Similarly, the human tongue finds use in the detection of organ dysfunction with tongue reflexology. OBJECTIVE: To detect diabetes at an early stage using a non-invasive method of diabetes detection through tongue images and to utilize the reasonable cost of modality (SEM camera) for capturing the tongue images instead of the existing and expensive imaging modalities like X-ray, Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography, Single-Photon Emission Computed Tomography etc. Methods: The tongue image is captured via SEM camera, it is preprocessed to remove noise and resize the tongue such that it is suitable for segmentation. Greedy Snake Algorithm (GSA) is used to segment the tongue image. The texture features of the tongue are analyzed and finally it is classified as diabetic or normal. RESULTS: Failure of organs stomach, intestine, liver and pancreas results in change of the color of the tongue, coating thickness and cracks on the tongue. Changes in pancreas proactive behavior also reflect on tongue coating. The tongue coating texture varies from white or vanilla to yellow also the tongue coating thickness also increases. CONCLUSION: In this paper, the author proposes to diagnose Diabetes Type2 (DT2) at an early stage from tongue digital image. The tongue image is acquired and processed with Greedy Snake Algorithm (GSA) to extract edge and texture features.

Comparative analysis of classification based algorithms for diabetes diagnosis using iris images.

Photo-diagnosis is always an intriguing area for the researchers, with the advancement of image processing and computer machine vision techniques it have become more reliable and popular in recent years. The objective of this paper is to study the change in the features of iris, particularly irregularities in the pigmentation of certain areas of the iris with respect to diabetic health of an individual. Apart from the point that iris recognition concentrates on the overall structure of the iris, diagnostic techniques emphasises the local variations in the particular area of iris. Pre-image processing techniques have been applied to extract iris and thereafter, region of interest from the extracted iris have been cropped out. In order to observe the changes in the tissue pigmentation of region of interest, statistical, texture textural and wavelet features have been extracted. At the end, a comparison of accuracies of five different classifiers has been presented to classify two subject groups of diabetic and non-diabetic. Best classification accuracy has been calculated as 89.66% by the random forest classifier. Results have been shown the effectiveness and diagnostic significance of the proposed methodology. Presented piece of work offers a novel systemic perspective of non-invasive and automatic diabetic diagnosis.

A concept for holistic whole body MRI data analysis, Imiomics.

PURPOSE: To present and evaluate a whole-body image analysis concept, Imiomics (imaging-omics) and an image registration method that enables Imiomics analyses by deforming all image data to a common coordinate system, so that the information in each voxel can be compared between persons or within a person over time and integrated with non-imaging data. METHODS: The presented image registration method utilizes relative elasticity constraints of different tissue obtained from whole-body water-fat MRI. The registration method is evaluated by inverse consistency and Dice coefficients and the Imiomics concept is evaluated by example analyses of importance for metabolic research using non-imaging parameters where we know what to expect. The example analyses include whole body imaging atlas creation, anomaly detection, and cross-sectional and longitudinal analysis. RESULTS: The image registration method evaluation on 128 subjects shows low inverse consistency errors and high Dice coefficients. Also, the statistical atlas with fat content intensity values shows low standard deviation values, indicating successful deformations to the common coordinate system. The example analyses show expected associations and correlations which agree with explicit measurements, and thereby illustrate the usefulness of the proposed Imiomics concept. CONCLUSIONS: The registration method is well-suited for Imiomics analyses, which enable analyses of relationships to non-imaging data, e.g. clinical data, in new types of holistic targeted and untargeted big-data analysis.

Weighted averaging in spectroscopic studies improves statistical power.

PURPOSE: In vivo MRS is often characterized by a spectral signal-to-noise ratio (SNR) that varies highly between experiments. A common design for spectroscopic studies is to compare the ratio of two spectral peak amplitudes between groups, e.g. individual PCr/γ-ATP ratios in 31 P-MRS. The uncertainty on this ratio is often neglected. We wished to explore this assumption. THEORY: The canonical theory for the propagation of uncertainty on the ratio of two spectral peaks and its incorporation in the Frequentist hypothesis testing framework by weighted averaging is presented. METHODS: Two retrospective re-analyses of studies comparing spectral peak ratios and one prospective simulation were performed using both the weighted and unweighted methods. RESULTS: It was found that propagating uncertainty correctly improved statistical power in all cases considered, which could be used to reduce the number of subjects required to perform an MR study. CONCLUSION: The variability of in vivo spectroscopy data is often accounted for by requiring it to meet an SNR threshold. A theoretically sound propagation of the variable uncertainty caused by quantifying spectra of differing SNR is therefore likely to improve the power of in vivo spectroscopy studies. Magn Reson Med 78:2082-2094, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Prediction of Femoral Neck Strength in Patients with Diabetes Mellitus with Trabecular Bone Analysis and Tomosynthesis Images.

Purpose To determine trabecular bone analysis values by using tomosynthesis images in determining femoral neck strength in patients with diabetes mellitus and compare its parameters between vertebral compression fracture and nonfracture groups. Materials and Methods The institutional review board approved this study, and written informed consent was obtained from all patients. Forty-nine patients with diabetes mellitus were included. Within 1 week, patients underwent dual x-ray absorptiometry (DXA), tomosynthesis, and computed tomography (CT) covering the T10 vertebral body to the hip joints. The trabecular patterns of tomosynthesis images were extracted, and the total strut length, bone volume per tissue volume, and five textural features (homogeneity, entropy, correlation, contrast, and variance) were obtained as the indices of tomosynthesis images. Failure load of the femoral neck, which was determined with the CT-based finite-element method (FEM), was used as the reference standard for bone strength. A forward stepwise multiple regression analysis for evaluating the availability of the tomosynthesis image indices was performed. The bone mineral density (BMD) at DXA and tomosynthesis image indices were compared between the vertebral compression fracture (n = 16) and nonfracture groups (n = 33) according to Genant semiquantitative morphometry methods by using one-way analysis of variance. Results The combination of BMD with the bone volume per tissue volume at the principal tensile group and the correlation at the principal compressive group showed the highest correlation to the failure load at CT FEM, and the correlation (r2 = 0.83) was higher than that between the failure load and the BMD alone (r2 = 0.76; P < .001). The averages of the bone volume per tissue volume and entropy at the principal tensile group in the vertebral compression fracture group were lower than those in the nonfracture group (P = .017 and P = .029, respectively), but there was no difference in BMD. Conclusion Tomosynthesis-based trabecular bone analysis is technically feasible and, in combination with BMD measurements, can potentially be used to determine bone strength in patients with diabetes mellitus. © RSNA, 2016 Online supplemental material is available for this article.

Acarbose: oral anti-diabetes drug with additional cardiovascular benefits.

Acarbose is an alpha-glucosidase inhibitor acting specifically at the level of postprandial glucose excursion. This compound lowers HbA(1c) by 0.5-1% in patients with Type 2 diabetes, either drug naive or in combination with other antidiabetic drugs. In those with impaired glucose tolerance (IGT), it reduces the incidence of newly diagnosed diabetes by 36.4%. Furthermore, it has beneficial effects on overweight, reduces blood pressure and triglycerides, and downregulates biomarkers of low-grade inflammation. In the Study To Prevent Non-Insulin-Dependent-Diabetes-Mellitus (STOP-NIDDM) trial, acarbose significantly reduced the progression of intima media thickness, incidence of cardiovascular events and of newly diagnosed hypertension. In a meta-analysis of patients with Type 2 diabetes (MERIA), acarbose intake was associated with a reduction of cardiovascular events by 35%. Acarbose is a very safe drug but in approximately 30% of patients, it can cause gastrointestinal complaints due to its mode of action, which in the majority disappear after 1-2 months. Acarbose is approved for treatment of IGT in 25 countries. It can be given alone or in combination with other oral antidiabetics and insulin. Acarbose is particularly effective in those with IGT and early diabetes and patients with comorbidities of the metabolic syndrome.