Artificial IntelligenceNCT05223712
Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information
An observational study of Artificial Intelligence and Ophthalmology, run by Sun Yat-sen University at 1 site in China.
Trial summary
This is an retrospective and prospective multicenter study to develop and validate an artificial intelligent (AI) aided diagnosis, therapeutic effect assessment model including chronic kidney disease (CKD) and dialysis patients starting from April 2009, which is based on ophthalmic examinations (e.g. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Ophthalmology, Kidney Diseases
- What is being tested?
- Diagnostic Test: Chronic Kidney Diseases
- How many people
- 4,000 participants
- Where
- 1 siteChina
- Study dates
- 28 Aug 2021to Dec 2022
- 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 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Participants who had slit-lamp, retinal fundus photography and kidney disease tests at the Department of Nephrology, First Affiliated Hospital of Sun Yat-sen University and Medical Centre of Aikang Health Care, Guangzhou, China
Who may be able to join
- Patients previously received kidney biopsy, ophthalmic examinations and routine examinations of the department of nephrology during in-hospital period with BCVA>0.5.
Who cannot join
- Patients without retinal fundus images or kidney diseases.
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, 510060
Haotian Lin, M.D., Ph.D, +8613802793086, haot.lin@hotmail.com
Qianni Wu, M.D., Ph.D, +8615521506995, wuqianni@gzzoc.com
About this study
This is an retrospective and prospective multicenter study to develop and validate an artificial intelligent (AI) aided diagnosis, therapeutic effect assessment model including chronic kidney disease (CKD) and dialysis patients starting from April 2009, which is based on ophthalmic examinations (e.g. retinal fundus photography, slit-lamp images, OCTA, etc.) and CKD diagnostic and therapeutic data (routine clinical evaluations and laboratory data), to provide a reliable basis and guideline for clinical diagnosis and treatment.
- Conditions
- Artificial Intelligence, Ophthalmology, Kidney Diseases
- Being tested
- Diagnostic Test: Chronic Kidney Diseases
- Keywords
- Kidney Diseases, Artificial Intelligence, Eye information
- Medical subject headings
- Kidney Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diagnostic Test: Chronic Kidney DiseasesOther
The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: Other: Diagnostic Test: Chronic Kidney Diseases | |
| Development Dataset 02 | Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China Receives: Other: Diagnostic Test: Chronic Kidney Diseases | |
| 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 Receives: Other: Diagnostic Test: Chronic Kidney Diseases | |
| Validation Dataset 02 | Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China Receives: Other: Diagnostic Test: Chronic Kidney Diseases | |
| 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 Receives: Other: Diagnostic Test: Chronic Kidney Diseases | |
| Test Dataset 02 | Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China Receives: Other: Diagnostic Test: Chronic Kidney Diseases |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve of the deep learning system
The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors
Measured: baseline
Secondary outcomes (1)
Sensitivity and specificity of the deep learning system
The investigators will calculate the sensitivity and specifity of deep learning system and compare this index between deep learning system and human doctors
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Feb 2022
- Study start28 Aug 2021
- Primary completionDec 2022 (estimated)
- Study completionDec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe 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
- Cohort
- Time perspective
- Other
- Enrolment
- 4,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- First Affiliated Hospital, Sun Yat-Sen University
- Responsible party
- Principal investigator, Haotian Lin, Sun Yat-sen University
- Investigators
- Yizhi Liu, M.D., Ph.D., Study chair, Zhongshan Ophthalmic Center, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05223712
- Sponsor's protocol ID
- AIKD-2021
- Status last verified
- Jan 2022
- First submitted
- 23 Jan 2022
- Last updated
- 4 Feb 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 Feb 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information
- ClinicalTrials.gov ID
- NCT05223712
- Acronym / sponsor ID
- AIKD-2021
- Conditions
- Artificial Intelligence, Ophthalmology, Kidney Diseases
- Keywords
- Kidney Diseases, Artificial Intelligence, Eye information
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- First Affiliated Hospital, Sun Yat-Sen University
- Study start
- 28 Aug 2021 (actual)
- Primary completion
- Dec 2022 (estimated)
- Study completion
- Dec 2022 (estimated)
- First posted
- 4 Feb 2022 (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.
This is an retrospective and prospective multicenter study to develop and validate an artificial intelligent (AI) aided diagnosis, therapeutic effect assessment model including chronic kidney disease (CKD) and dialysis patients starting from April 2009, which is based on ophthalmic examinations (e.g. retinal fundus photography, slit-lamp images, OCTA, etc.) and CKD diagnostic and therapeutic data (routine clinical evaluations and laboratory data), to provide a reliable basis and guideline for clinical diagnosis and treatment.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 6
| Arm | Type | Receives |
|---|---|---|
| Development Dataset 01 | – | Other: Diagnostic Test: Chronic Kidney DiseasesSlit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University |
| Development Dataset 02 | – | Other: Diagnostic Test: Chronic Kidney DiseasesSlit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China |
| Validation Dataset 01 | – | Other: Diagnostic Test: Chronic Kidney DiseasesSlit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University |
| Validation Dataset 02 | – | Other: Diagnostic Test: Chronic Kidney DiseasesSlit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China |
| Test Dataset 01 | – | Other: Diagnostic Test: Chronic Kidney DiseasesSlit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University |
| Test Dataset 02 | – | Other: Diagnostic Test: Chronic Kidney DiseasesSlit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Participants who had slit-lamp, retinal fundus photography and kidney disease tests at the Department of Nephrology, First Affiliated Hospital of Sun Yat-sen University and Medical Centre of Aikang Health Care, Guangzhou, China
Key inclusion criteria (1 listed)
- Patients previously received kidney biopsy, ophthalmic examinations and routine examinations of the department of nephrology during in-hospital period with BCVA>0.5.
Key exclusion criteria (3 listed)
- Patients without retinal fundus images or kidney diseases.
- 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.
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 |
|---|---|---|
| Diagnostic Test: Chronic Kidney DiseasesThe development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets. | Other | Development Dataset 01, Development Dataset 02, Test Dataset 01, Test Dataset 02, Validation Dataset 01, Validation Dataset 02 |
Primary outcome
Area under the receiver operating characteristic curve of the deep learning system
Time frame: baseline
The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors
1 secondary outcome
Sensitivity and specificity of the deep learning system
Time frame: baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Feb 2022.
- Individual participant dataSharing planned: No
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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.
Yizhi Liu, M.D., Ph.D. study chair
Zhongshan Ophthalmic Center, Sun Yat-sen University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence and Ophthalmology, run by Sun Yat-sen University at 1 site 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 1 inclusion and 3 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: status not confirmed, as of the sponsor's last update on 4 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 1 site 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Haotian Lin, Ph. D (13802793086, gddlht@aliyun.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the receiver operating characteristic curve of the deep learning system (time frame: baseline). 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 4 Feb 2022.