Automatic Segmentation Ultrasound-based Radiomics Technology in Diabetic Kidney Disease

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Diabetic Kidney Disease

Intervention / Treatment
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* Diagnostic Test: ultrasonic imaging


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental group<br>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. | Diagnostic Test: ultrasonic imaging<br>* Two-dimensional ultrasound images of the patient's kidneys were obtained by ultrasound imaging.<br>|
| Control group<br>Control1：T2DM patients with Type 2 diabetes Control2：Low level DKD patients with diabetic kidney disease Stage I and II. | Diagnostic Test: ultrasonic imaging<br>* Two-dimensional ultrasound images of the patient's kidneys were obtained by ultrasound imaging.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Miou | The mean intersection over union (Miou) of DL-based auto-segmentation in different medical centers | 6 months | 
| mPA | The mean pixel accuracy (mPA) of DL-based auto-segmentation in different medical centers | 6 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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diabetic kidney disease, ultrasound, radiomics, deep learning, multi-center