Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information
NCT ID: NCT05223712
Last Updated: 2022-02-04
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
UNKNOWN
4000 participants
OBSERVATIONAL
2021-08-28
2022-12-31
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Renal Cancer Detection Using Convolutional Neural Networks
NCT03857373
Assessment of AI Prediction Models in Prediction of Acute Kidney Injury in Critical Patients
NCT06857188
A Real World, Multi-centric, Observational Registry Study of Chronic Kidney Diseases
NCT05188885
Establish and Apply the Evaluation System of Ultrasonic Integrated Technology for Prevention and Treatment of Acute Kidney Injury
NCT02347930
Clinical Values of Automated Electronic Alert for Acute Kidney Injury
NCT02793167
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
COHORT
OTHER
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
Development Dataset 01
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University
Diagnostic Test: Chronic Kidney Diseases
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
Development Dataset 02
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China
Diagnostic Test: Chronic Kidney Diseases
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
Validation Dataset 01
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University
Diagnostic Test: Chronic Kidney Diseases
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
Validation Dataset 02
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China
Diagnostic Test: Chronic Kidney Diseases
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
Test Dataset 01
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University
Diagnostic Test: Chronic Kidney Diseases
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
Test Dataset 02
Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China
Diagnostic Test: Chronic Kidney Diseases
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
Diagnostic Test: Chronic Kidney Diseases
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
Exclusion Criteria
* The quality of the retinal fundus images can not meet the requirement for furthur analysis.
* Severe loss of results of routine examinations of the department of nephrology.
18 Years
ALL
Yes
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
First Affiliated Hospital, Sun Yat-Sen University
OTHER
Sun Yat-sen University
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Haotian Lin
Principal Investigator
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Yizhi Liu, M.D., Ph.D.
Role: STUDY_CHAIR
Zhongshan Ophthalmic Center, Sun Yat-sen University
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, China
Countries
Review the countries where the study has at least one active or historical site.
Central Contacts
Reach out to these primary contacts for questions about participation or study logistics.
Facility Contacts
Find local site contact details for specific facilities participating in the trial.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
AIKD-2021
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.