Automatic Segmentation Ultrasound-based Radiomics Technology in Diabetic Kidney Disease

Diabetic Kidney Disease Clinical Trial

Official title:

Noninvasive Detection of Diabetic Kidney Disease Based on Automatic Segmentation Ultrasound-based Radiomics Technology

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05025540
• Other study ID #: 2021-0465
• Status: Completed
• Start date: June 1, 2021
• Est. completion date: December 1, 2021

Study information

• Verified date: August 2021
• Source: Second Affiliated Hospital, School of Medicine, Zhejiang University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 499
• Est. completion date: December 1, 2021
• Est. primary completion date: December 1, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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.

Related Conditions & MeSH terms

• Diabetic Kidney Disease
• Diabetic Nephropathies
• Kidney Diseases

Intervention

Diagnostic Test:
• ultrasonic imaging
Two-dimensional ultrasound images of the patient's kidneys were obtained by ultrasound imaging.

Locations

Country	Name	City	State
China	The People's Hospital of Yingshang	Fuyang	Anhui
China	Department of Ultrasound, Second Affiliated Hospital, School of Medicine, Zhejiang University	Hangzhou	Zhejiang
China	Tianjin Third Central Hospital	Tianjin	Tianjin

Sponsors (1)

Lead Sponsor	Collaborator
Second Affiliated Hospital, School of Medicine, Zhejiang University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	AUC	The area under curve (AUC) of radiomics model for differentiating DKD and T2DM or high level and low level DKD patients	6 months
Secondary	Miou	The mean intersection over union (Miou) of DL-based auto-segmentation in different medical centers	6 months
Secondary	mPA	The mean pixel accuracy (mPA) of DL-based auto-segmentation in different medical centers	6 months