Automatic Segmentation Ultrasound-based Radiomics Technology in Diabetic Kidney Disease

NCT ID: NCT05025540

Last Updated: 2022-02-16

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 499 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-06-01
• Study Completion Date: 2021-12-01

Brief Summary

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.

Detailed 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.

Conditions

Diabetic Kidney Disease

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: RETROSPECTIVE

Study Groups

Experimental group

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.

ultrasonic imaging

Intervention Type: DIAGNOSTIC_TEST

Two-dimensional ultrasound images of the patient's kidneys were obtained by ultrasound imaging.

Control group

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

ultrasonic imaging

Intervention Type: DIAGNOSTIC_TEST

Two-dimensional ultrasound images of the patient's kidneys were obtained by ultrasound imaging.

Interventions

ultrasonic imaging

Two-dimensional ultrasound images of the patient's kidneys were obtained by ultrasound imaging.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Second Affiliated Hospital, School of Medicine, Zhejiang University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Pintong Huang

Role: STUDY_CHAIR

Department of Ultrasound, The Second Affiliated Hospital of Zhejiang University

Locations

The People's Hospital of Yingshang

Fuyang, Anhui, China

Tianjin Third Central Hospital

Tianjin, Tianjin Municipality, China

Department of Ultrasound, Second Affiliated Hospital, School of Medicine, Zhejiang University

Hangzhou, Zhejiang, China

Countries

China

Other Identifiers

2021-0465

Identifier Type: -

Identifier Source: org_study_id