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Clinical Trial Summary

Diabetic kidney disease is a common complication of diabetes and the main cause of end-stage renal disease. Clinically, most patients with DKD have albuminuria, but some patients with DKD have normal urinary albumin, even until they develop stage 5 diabetic kidney disease. In these diabetic patients with normal urinary albumin, relatively intact renal function and no significant renal damage (abnormal urine sediment and electrolyte) were observed on laboratory tests. Whether imaging can detect kidney injury sensitively is an urgent problem in clinical practice.

Clinical Trial Description

Ultrasound examination is a convenient, cheap and non-invasive method for kidney examination. However, the ability of conventional ultrasound to distinguish diabetic kidney disease from normal kidney is limited, and it is difficult to accurately distinguish between diabetic kidney disease and normal kidney only with the naked eye. In recent years, computer science has developed rapidly and artificial intelligence has been developing continuously. Much progress has been made in applying ARTIFICIAL intelligence to data analysis. Machine learning is a direction of generalized artificial intelligence, its main characteristic is to make the machine autonomous prediction and create algorithm, so as to achieve autonomous learning. kidney disease and deep learning are two different approaches in the field of machine learning. In this study, image omics and deep learning were used to analyze the images. Image omics extracts traditional image features, including shape, gray scale, texture, etc., and uses machine learning (pattern recognition) models to classify and predict, such as support vector machine, random forest, XGBoost, etc. Deep learning directly uses the convolutional network CNN to extract features, and completes classification and prediction in combination with the full connection layer, etc. This study aims to explore the detection of diabetic kidney disease and its pathological degree based on self-divided ultrasound image omics, maximize the mining of internal information of ultrasound images, and form a set of non-invasive monitoring of diabetic kidney disease complications development system, especially in primary medical institutions, has a broad clinical application prospect. ;

Study Design

Related Conditions & MeSH terms

NCT number NCT05025540
Study type Observational
Source Second Affiliated Hospital, School of Medicine, Zhejiang University
Contact Pintong Huang
Phone +8618857168333
Email [email protected]
Status Recruiting
Start date June 1, 2021
Completion date December 31, 2021

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