AIMC Topic: Diabetes Mellitus
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A novel stacking technique for prediction of diabetes.
Computers in biology and medicine
Jun 8, 2021
BACKGROUND: Machine Learning (ML) represents a rapidly growing technology that supplies the most effective solutions for solving complex problems. The application of ML techniques in healthcare is gaining more attention because of ML-associated autom...
Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients.
Sensors (Basel, Switzerland)
Jun 3, 2021
Diabetic retinopathy (DR) is the main cause of blindness in diabetic patients. Early and accurate diagnosis can improve the analysis and prognosis of the disease. One of the earliest symptoms of DR are the hemorrhages in the retina. Therefore, we pro...
Functional link convolutional neural network for the classification of diabetes mellitus.
International journal for numerical methods in biomedical engineering
May 31, 2021
Diabetes is a faction of metabolic ailments distinguished by hyperglycemia which is the consequence of a defect, in the action of insulin, insulin secretion, or both and producing various abnormalities in the human body. In recent years, the utilizat...
Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning.
Sensors (Basel, Switzerland)
May 26, 2021
Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stoppi...
Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning.
BMC endocrine disorders
May 4, 2021
INTRODUCTION: Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and v...
Efficient Automated Disease Diagnosis Using Machine Learning Models.
Journal of healthcare engineering
May 4, 2021
Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease may control the death rate due to these diseases. In this paper, an efficient automated disease diagno...
Prediction of brain age from routine T2-weighted spin-echo brain magnetic resonance images with a deep convolutional neural network.
Neurobiology of aging
Apr 28, 2021
Our study investigated the feasibility and clinical relevance of brain age prediction using axial T2-weighted images (T2-WIs) with a deep convolutional neural network (CNN) algorithm. The CNN model was trained by 1,530 scans in our institution. The p...
Iterative guided machine learning-assisted systematic literature reviews: a diabetes case study.
Systematic reviews
Apr 2, 2021
BACKGROUND: Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their val...
Subfoveal choroidal thickness changes after intravitreal ranibizumab injections in different patterns of diabetic macular edema using a deep learning-based auto-segmentation.
International ophthalmology
Mar 30, 2021
PURPOSE: To evaluate the effect of intravitreal injection of ranibizumab (IVR) on subfoveal choroidal thickness (SFCT) and its relationship with central macular thickness (CMT) and best-corrected visual acuity (BCVA) changes in eyes with center-invol...
The effect of diabetes on major robotic hepatectomy.
Journal of robotic surgery
Mar 8, 2021
Studies regarding the influence of diabetes on perioperative outcomes after major hepatectomy are conflicting. The objective of this study is to analyze the effects of diabetes on patients undergoing robotic major hepatectomy. With Institutional Revi...
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