Diabetic RetinopathyNCT03911323
Real-world Diagnostic Effectiveness of Artificial Intelligence Algorithm in Diabetic Retinopathy Screening
An observational study of Diabetic Retinopathy, run by Shenzhen Second People's Hospital at 1 site in China.
Trial summary
Recently, artificial intelligence algorithm has made great progress in the prediction of diabetic retinopathy based on fundus images，showing very high sensitivity and specificity. However，the real-world diagnosis effectiveness of deep learning model is still unclear. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Diabetic Retinopathy
- What is being tested?
- diabetic retinopathy
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Oct 2018to 1 Oct 2020
- 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
- No healthy volunteers
- Adult, Older adult
Study population: Diabetic patients who meet the eligibility criteria.
Who may be able to join
- Subject must understand the study, participate voluntarily, and has signed informed consent
- Age 18 or older, no limitations on gender identity
- Patients with type 1 or type 2 diabetes.
Who cannot join
- Subjects diagnosed with eye diseases other than diabetic retinopathy
- Subjects diagnosed with macular edema, severe non-proliferative retinopathy, proliferative retinopathy, radioactive retinopathy or retinal vein obstruction.
- Pregnant woman, subjects with mydriatic allergy, unclear refractive medium, family history of glaucoma, or diagnosed as narrow angle
- Subjects with a history of laser therapy, retinal surgery or anti-vascular endothelial growth factor injection
- Subjects currently participating in another ophthalmic research, receiving ophthalmic research products.
- Subject who is photo-sensitivity or taking medication that causes photosensitivity
- Subjects received photodynamic therapy recently
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Shenzhen second peoples's hospital
Shenzhen, Guangdong, 518000
Lisha Mou, PhD, +86-1589-9876-015, lishamou@gmail.com
About this study
Official title: A Prospective Clinical Study on the Real-world Diagnostic Effectiveness of Artificial Intelligence Algorithm in Diabetic Retinopathy Screening
Recently, artificial intelligence algorithm has made great progress in the prediction of diabetic retinopathy based on fundus images，showing very high sensitivity and specificity. However，the real-world diagnosis effectiveness of deep learning model is still unclear.
This study is designed to evaluate the clinical efficacy of such an algorithm in detecting referable diabetic retinopathy.
Read the detailed description
This prospective clinical study is designed to evaluate the real-world diagnostic performance of an AI model in detecting referable diabetic retinopathy (RDR, defined as more than mild NPDR), by evaluating its sensitivity and specificity compared to the clinical reference standard- seven-field stereoscopic photography.
The subjects enrolled in this study are patients with T1DM or T2DM. Qualified color fundus images and seven-field stereoscopic photography images of each eyes of the subject are taken. The fundus images are graded for RDR by the algorithm under test, and seven-field stereoscopic photography images of the same eye are graded by ophthalmologist, which serving as the gold standard to compare the algorithm performance against.
The trial plans to enroll 1000 subjects. With a 95% confidence interval, the sensitivity is expected to be at least 87% whereas the specificity at 89% or above.
The quality of fundus images are assessed according to the National DR Screening Imaging and Grading Guideline published by Chinese Ophthalmological Society and Chinese Medical Doctor Association in 2017.
The grading of RDR is based on the National DR Clinical Diagnosis and Treatment Guideline published by Chinese Ophthalmological Society in 2014.
A brief overview of the clinical protocol is as follows:
Candidate recruiting phase: recruiting qualified participants; Clinical phase: imaging and diagnosing by AI and ophthalmologist ; Statistical analysis phase: comparing two outputs; Closing phase: final report and archiving
- Conditions
- Diabetic Retinopathy
- Being tested
- diabetic retinopathy
- Medical subject headings
- Diabetic Retinopathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
diabetic retinopathyOther
Patients with diabetes enrolled will undergo nonmydriatic fundus imaging and seven-field stereoscopic photography. The images will be run on an artificial intelligence (AI) algorithm. The diagnosis of the AI algorithm will be compared to the diagnosis of seven-field stereoscopic photography by ophthalmologist. Sensitivity and specificity will be calculated to evaluate the performance of AI algorithm.
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity and specificity
To evaluate the sensitivity and specificity of the model in detecting referable DR (more than mild NPDR)
Measured: No more than 1 day for each subject
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Apr 2019
- Study start1 Oct 2018
- Primary completion1 Oct 2020 (estimated)
- Study completion1 Oct 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetic RetinopathyStudies of the same condition, open and under way.
- 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 only
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shenzhen Second People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Lisha Mou, PhD, Principal investigator, Shenzhen Second People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03911323
- Sponsor's protocol ID
- 20190401
- Status last verified
- Apr 2019
- First submitted
- 9 Apr 2019
- Last updated
- 11 Apr 2019
- Results posted
- No
- 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 11 Apr 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective Clinical Study on the Real-world Diagnostic Effectiveness of Artificial Intelligence Algorithm in Diabetic Retinopathy Screening
- ClinicalTrials.gov ID
- NCT03911323
- Acronym / sponsor ID
- 20190401
- Conditions
- Diabetic Retinopathy
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Shenzhen Second People's HospitalUniversity, hospital or other
- Study start
- 1 Oct 2018 (actual)
- Primary completion
- 1 Oct 2020 (estimated)
- Study completion
- 1 Oct 2020 (estimated)
- First posted
- 11 Apr 2019 (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.
Recently, artificial intelligence algorithm has made great progress in the prediction of diabetic retinopathy based on fundus images，showing very high sensitivity and specificity. However，the real-world diagnosis effectiveness of deep learning model is still unclear.
This study is designed to evaluate the clinical efficacy of such an algorithm in detecting referable diabetic retinopathy.
Detailed description
This prospective clinical study is designed to evaluate the real-world diagnostic performance of an AI model in detecting referable diabetic retinopathy (RDR, defined as more than mild NPDR), by evaluating its sensitivity and specificity compared to the clinical reference standard- seven-field stereoscopic photography.
The subjects enrolled in this study are patients with T1DM or T2DM. Qualified color fundus images and seven-field stereoscopic photography images of each eyes of the subject are taken. The fundus images are graded for RDR by the algorithm under test, and seven-field stereoscopic photography images of the same eye are graded by ophthalmologist, which serving as the gold standard to compare the algorithm performance against.
The trial plans to enroll 1000 subjects. With a 95% confidence interval, the sensitivity is expected to be at least 87% whereas the specificity at 89% or above.
The quality of fundus images are assessed according to the National DR Screening Imaging and Grading Guideline published by Chinese Ophthalmological Society and Chinese Medical Doctor Association in 2017.
The grading of RDR is based on the National DR Clinical Diagnosis and Treatment Guideline published by Chinese Ophthalmological Society in 2014.
A brief overview of the clinical protocol is as follows:
Candidate recruiting phase: recruiting qualified participants; Clinical phase: imaging and diagnosing by AI and ophthalmologist ; Statistical analysis phase: comparing two outputs; Closing phase: final report and archiving
Study design
- Observational model
- Case only
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Diabetic patients who meet the eligibility criteria.
Key inclusion criteria (3 listed)
- Subject must understand the study, participate voluntarily, and has signed informed consent
- Age 18 or older, no limitations on gender identity
- Patients with type 1 or type 2 diabetes.
Key exclusion criteria (7 listed)
- Subjects diagnosed with eye diseases other than diabetic retinopathy
- Subjects diagnosed with macular edema, severe non-proliferative retinopathy, proliferative retinopathy, radioactive retinopathy or retinal vein obstruction.
- Pregnant woman, subjects with mydriatic allergy, unclear refractive medium, family history of glaucoma, or diagnosed as narrow angle
- Subjects with a history of laser therapy, retinal surgery or anti-vascular endothelial growth factor injection
- Subjects currently participating in another ophthalmic research, receiving ophthalmic research products.
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 |
|---|---|---|
| diabetic retinopathyPatients with diabetes enrolled will undergo nonmydriatic fundus imaging and seven-field stereoscopic photography. The images will be run on an artificial intelligence (AI) algorithm. The diagnosis of the AI algorithm will be compared to the diagnosis of seven-field stereoscopic photography by ophthalmologist. Sensitivity and specificity will be calculated to evaluate the performance of AI algorithm. | Other | – |
Primary outcome
Sensitivity and specificity
Time frame: No more than 1 day for each subject
To evaluate the sensitivity and specificity of the model in detecting referable DR (more than mild NPDR)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Apr 2019.
- 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.
Lisha Mou, PhD principal investigator
Shenzhen Second People's Hospital
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 Diabetic Retinopathy, run by Shenzhen Second People's Hospital 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 3 inclusion and 7 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 11 Apr 2019. 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 Jiying Hu, PhD (+86-1808-6481-821, 18086481821@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity and specificity (time frame: No more than 1 day for each subject). 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 11 Apr 2019.