Clinical trial NCT05025540: Automatic Segmentation Ultrasound-based Radiomics Technology in Diabetic Kidney Disease
Official title: Noninvasive Detection of Diabetic Kidney Disease Based on Automatic Segmentation Ultrasound-based Radiomics Technology
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Diabetic Kidney Disease
Keywords: diabetic kidney disease
ultrasound
radiomics
deep learning
multi-center
Lead sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Sponsor class: OTHER
First posted: 2021-08-27
Enrollment: 499
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 1. patients with diabetes
2. patients with or without DKD

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.

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.

Primary outcomes:
measure: AUC
timeFrame: 6 months
Secondary outcomes:
measure: Miou
timeFrame: 6 months
measure: mPA
timeFrame: 6 months
Other outcomes:
Locations:
city: Fuyang
country: China
facility: The People's Hospital of Yingshang
state: Anhui
city: Tianjin
country: China
facility: Tianjin Third Central Hospital
state: Tianjin
city: Hangzhou
country: China
facility: Department of Ultrasound, Second Affiliated Hospital, School of Medicine, Zhejiang University
state: Zhejiang
Study officials:
affiliation: Department of Ultrasound, The Second Affiliated Hospital of Zhejiang University
name: Pintong Huang
role: STUDY_CHAIR
References: