Diabetic Kidney DiseaseNCT05025540
Automatic Segmentation Ultrasound-based Radiomics Technology in Diabetic Kidney Disease
An observational study of Diabetic Kidney Disease, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 3 sites in China.
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… Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Diabetic Kidney Disease
- What is being tested?
- ultrasonic imaging
- Type of treatment
- Injection or infusion
- How many people
- 499 participants
- Where
- 3 sitesChina
- Study dates
- 1 Jun 2021to 1 Dec 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 1. patients with diabetes 2. patients with or without DKD
Who may be able to join
- 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.
Who cannot join
- 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 locations and contacts
3 sites in 1 country.
China3 sites
The People's Hospital of Yingshang
Fuyang, Anhui, 236200
Department of Ultrasound, Second Affiliated Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, 310000
Tianjin Third Central Hospital
Tianjin, Tianjin Municipality, 300000
About this study
Official title: Noninvasive Detection of Diabetic Kidney Disease Based on Automatic Segmentation Ultrasound-based Radiomics Technology
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.
Read the 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
- Being tested
- ultrasonic imaging
- Keywords
- diabetic kidney disease, ultrasound, radiomics, deep learning, multi-center
- Medical subject headings
- Diabetic Nephropathies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ultrasonic imagingDiagnostic test
Two-dimensional ultrasound images of the patient's kidneys were obtained by ultrasound imaging.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: ultrasonic imaging | |
| Control group | Control1：T2DM patients with Type 2 diabetes Control2：Low level DKD patients with diabetic kidney disease Stage I and II. Receives: Diagnostic Test: ultrasonic imaging |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC
The area under curve (AUC) of radiomics model for differentiating DKD and T2DM or high level and low level DKD patients
Measured: 6 months
Secondary outcomes (2)
Miou
The mean intersection over union (Miou) of DL-based auto-segmentation in different medical centers
Measured: 6 months
mPA
The mean pixel accuracy (mPA) of DL-based auto-segmentation in different medical centers
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Aug 2021
- Study start1 Jun 2021
- Primary completion1 Dec 2021
- Study completion1 Dec 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetic Kidney DiseaseStudies of the same condition, open and under way.
- More trials run by Second Affiliated Hospital, Zhejiang University, School of MedicineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 499 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Pintong Huang, Study chair, Department of Ultrasound, The Second Affiliated Hospital of Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05025540
- Sponsor's protocol ID
- 2021-0465
- Status last verified
- Aug 2021
- First submitted
- 24 Aug 2021
- Last updated
- 16 Feb 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 Feb 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Noninvasive Detection of Diabetic Kidney Disease Based on Automatic Segmentation Ultrasound-based Radiomics Technology
- ClinicalTrials.gov ID
- NCT05025540
- Acronym / sponsor ID
- 2021-0465
- Conditions
- Diabetic Kidney Disease
- Keywords
- diabetic kidney disease, ultrasound, radiomics, deep learning, multi-center
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 499 participants (actual)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Study start
- 1 Jun 2021 (actual)
- Primary completion
- 1 Dec 2021 (actual)
- Study completion
- 1 Dec 2021 (actual)
- First posted
- 27 Aug 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Experimental group | – | Diagnostic Test: ultrasonic imagingExperimental group1：DKD patients with Type 2 diabetes patients with DKD Experimental group2：High level DKD patients with diabetic kidney disease Stage III and IV. |
| Control group | – | Diagnostic Test: ultrasonic imagingControl1：T2DM patients with Type 2 diabetes Control2：Low level DKD patients with diabetic kidney disease Stage I and II. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 1. patients with diabetes 2. patients with or without DKD
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| ultrasonic imagingTwo-dimensional ultrasound images of the patient's kidneys were obtained by ultrasound imaging. | Diagnostic test | Control group, Experimental group |
Primary outcome
AUC
Time frame: 6 months
The area under curve (AUC) of radiomics model for differentiating DKD and T2DM or high level and low level DKD patients
2 secondary outcomes
Miou
Time frame: 6 months
mPA
Time frame: 6 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Feb 2022.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Pintong Huang study chair
Department of Ultrasound, The Second Affiliated Hospital of Zhejiang University
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetic Kidney Disease, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 3 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 16 Feb 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Feb 2022.