A Multi-center Study on the Artificial Intelligence Enabled Diabetic Retinopathy Screening Based on Fundus Images
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|ClinicalTrials.gov Identifier: NCT03602989|
Recruitment Status : Active, not recruiting
First Posted : July 27, 2018
Last Update Posted : May 20, 2019
Early detection and intervention of diabetic retinopathy (DR) is critical in preventing DR-related vision loss among type 1 (T1DM) and type 2 diabetic mellitus (T2DM) patients, currently estimated at over 100 million in China alone. Yet the healthcare resources, particularly retinal specialists, are in short supply and unevenly distributed. In order to help address this enormous mismatch and implement population-based screening, an artificial intelligence (AI) enabled, cloud based software is developed by training a custom-built convolutional neural network.
This study is designed to evaluate the safety and efficacy of such device in detecting referable diabetic retinopathy (moderate non-proliferative DR or worse).
|Condition or disease||Intervention/treatment|
|Diabetic Retinopathy||Device: AI-enabled Diabetic Retinopathy Screening Software|
This prospective, multi-center clinical study is designed to validate the performance of an AI enabled software - Shenzhen SiBright AIDRScreening - in detecting referable diabetic retinopathy (RDR, defined as more than mild NPDR) among study subjects primarily by evaluating its sensitivity and specificity.
The subjects enrolled in this trial are patients with T1DM and T2DM. For those who qualify, color fundus images of each eyes are taken and then independently graded for RDR by both the device under test and a centralized reading center, which, for the purpose of this trial, is the Image Reading Center at Zhongshan Ophthalmic Center, Sun Yat-sen University (ZIRC). The grading from ZIRC serves as the gold standard to compare the device performance against.
The trial plans to enroll 1000 subjects. With a 95% confidence interval, the sensitivity is expected to be at least 85% whereas the specificity at 90% or above.
Fundus image quality assessment is performed according to the National DR Screening Imaging and Grading Guideline jointly published by Chinese Ophthalmological Society and Chinese Medical Doctor Association in 2017.
The diagnosis 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 screening phase: recruiting qualified trial subjects;
- Clinical phase: imaging and grading by AI and ZIRC;
- Statistical analysis phase: comparing two outputs;
- Closing phase: final report and archiving
|Study Type :||Observational|
|Estimated Enrollment :||1000 participants|
|Official Title:||A Prospective, Multi-center Clinical Study on the Application of An Artificial Intelligence Enabled Disease Detection Software to Diabetic Retinopathy Screening Based on Fundus Images|
|Actual Study Start Date :||July 5, 2018|
|Actual Primary Completion Date :||November 30, 2018|
|Estimated Study Completion Date :||August 31, 2019|
- Device: AI-enabled Diabetic Retinopathy Screening Software
Color fundus images of both eyes are captured on site before being uploaded to and analyzed by the cloud-based Artificial Intelligence software
- Sensitivity and specificity [ Time Frame: No more than 1 day for each subject ]To evaluate the sensitivity and specificity of the device in detecting referable DR (more than mild NPDR)
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03602989
|Peking University People's Hospital|
|Beijing, Beijing, China|
|Zhongshan Ophthalmic Center|
|Guangzhou, Guangdong, China|
|The Eye Hospital of Wenzhou Medical University|
|Wenzhou, Zhejiang, China|
|Principal Investigator:||Xiaofeng Lin, M.D.||Zhongshan Ophthalmic Center, Sun Yat-sen University|