Automatic Segmentation Ultrasound-based Radiomics Technology in Diabetic Kidney Disease

Zhejiang University

Status: Completed

Conditions: Diabetic Kidney Disease

Treatments: Diagnostic Test: ultrasonic imaging

Study type: Observational

Funder types: Other

Identifiers

NCT05025540

2021-0465

Details and patient eligibility

About

Diabetic kidney disease is a common complication of diabetes and the main cause of end-stage renal disease. In this study, the investigator plan to enroll nearly 500 participant with/without DKD and to develop an automatic segmentation ultrasound based radiomics technology to differentiating participant with a non-invasive and an available way.

Full 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 in 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 automatic segmentation ultraound-based radiomics technology, 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.

Enrollment: 499 patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• patients with clinical diagnosis of T2DM and DKD were enrolled.
• patients with clear B mode ultrasound imaging in both side of kidney (left and right).
• No missing value in the vital clinical data such as eGFR and UACR.

Exclusion criteria

• Patients with large kidney space occupying disease such as kidney renal cyst and tumor were excluded.
• Ultrasound images with severe shadow or incomplete kidney border were excluded.

Trial design

499 participants in 2 patient groups

Experimental group

Description:

Experimental group1：DKD patients with Type 2 diabetes patients with DKD Experimental group2：High level DKD patients with diabetic kidney disease Stage III and IV.

Treatment:

Diagnostic Test: ultrasonic imaging

Control group

Description:

Control1：T2DM patients with Type 2 diabetes Control2：Low level DKD patients with diabetic kidney disease Stage I and II.

Treatment:

Diagnostic Test: ultrasonic imaging