Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic

In the medical field, experts’ knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning

Tubulointerstitial injury and proximal tubule albumin transport in early diabetic nephropathy induced by type 1 diabetes mellitus

A decrease in the tubular expression of albumin endocytic transporters megalin and cubilin has been associated with diabetic nephropathy (DN), but there are no comprehensive studies to date relating early tubulointerstitial injury and the effect of the disease on both transporters in type 1 diabetes mellitus (T1DM). We used eight-week-old male C57BL/6 mice divided into two groups; one of them received the vehicle (control group), while the other received the vehicle + 200 mg/kg streptozotocin (T1DM). Ten weeks after the injection, we evaluated plasma insulin, enzymuria, urinary vitamin D-binding protein (VDBP), tubulointerstitial fibrosis and proximal tubule histology, markers of autophagy, and megalin and cubilin levels. We found a reduction in tubular protein reabsorption (albumin and VDBP as specific substances carried by both transporters) with increased tubulointerstitial injury, development of fibrosis, thickening of tubular basement membrane, and an increase in tubular cell metalloproteases. This was associated with a decrease in the renal expression of megalin and cubilin. We also observed an increase in the amount of cellular vesicles of the phagocytic system in the tubules, which could be linked to an alteration of normal intracellular trafficking of both receptors, thus affecting the normal function of transporters in early stages of DN. In diabetic animals, the added effects of tubulointerstitial injury, the decreases in megalin and cubilin expression, and an altered intracellular trafficking of these receptors, seriously affect protein reabsorptionFil: Giraud Billoud, Maximiliano German. Universidad del Desarrollo. Facultad de Medicina Clínica Alemana; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Cienicas Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; ArgentinaFil: Ezquer, Fernando. Universidad del Desarrollo. Facultad de Medicina Clínica Alemana; ChileFil: Bahamonde, Javiera. Universidad del Desarrollo. Facultad de Medicina Clínica Alemana; ChileFil: Ezquer, Marcelo. Universidad del Desarrollo. Facultad de Medicina Clínica Alemana; Chil

Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine

In the field of predictive, preventive and personalised medicine, researchers are keen to identify novel and reliable ways to predict and diagnose disease, as well as to monitor patient response to therapeutic agents. In the last decade alone, the sensitivity of profiling technologies has undergone huge improvements in detection sensitivity, thus allowing quantification of minute samples, for example body fluids that were previously difficult to assay. As a consequence, there has been a huge increase in tear fluid investigation, predominantly in the field of ocular surface disease. As tears are a more accessible and less complex body fluid (than serum or plasma) and sampling is much less invasive, research is starting to focus on how disease processes affect the proteomic, lipidomic and metabolomic composition of the tear film. By determining compositional changes to tear profiles, crucial pathways in disease progression may be identified, allowing for more predictive and personalised therapy of the individual. This article will provide an overview of the various putative tear fluid biomarkers that have been identified to date, ranging from ocular surface disease and retinopathies to cancer and multiple sclerosis. Putative tear fluid biomarkers of ocular disorders, as well as the more recent field of systemic disease biomarkers, will be shown

Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis

Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the commonly used whole slide image (WSI) viewer, Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and display of neural network predictions on WSIs. Leveraging this, we propose the use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We track network performance improvements as a function of iteration and quantify the use of this pipeline for the segmentation of renal histologic findings on WSIs. More specifically, we present network performance when applied to segmentation of renal micro compartments, and demonstrate multi-class segmentation in human and mouse renal tissue slides. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.Comment: 15 pages, 7 figures, 2 supplemental figures (on the last page

Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks

We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN). Compared to existing DCNN-based DR detection methods, the proposed algorithm have the following advantages: (1) Our method can point out the location and type of lesions in the fundus images, as well as giving the severity grades of DR. Moreover, since retina lesions and DR severity appear with different scales in fundus images, the integration of both local and global networks learn more complete and specific features for DR analysis. (2) By introducing imbalanced weighting map, more attentions will be given to lesion patches for DR grading, which significantly improve the performance of the proposed algorithm. In this study, we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus images from Kaggle competition dataset. Under the guidance of clinical ophthalmologists, the experimental results show that our local lesion detection net achieve comparable performance with trained human observers, and the proposed imbalanced weighted scheme also be proved to significantly improve the capability of our DCNN-based DR grading algorithm