NCT03602989

A Multi-center Study on the Artificial Intelligence Enabled Diabetic Retinopathy Screening Based on Fundus Images

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 1,000 (estimated)
• Sponsor: Shenzhen SiBright Co., Ltd. · Industry
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

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).

Detailed description

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: 1. Candidate screening phase: recruiting qualified trial subjects; 2. Clinical phase: imaging and grading by AI and ZIRC; 3. Statistical analysis phase: comparing two outputs; 4. Closing phase: final report and archiving

Conditions

• Diabetic Retinopathy

Interventions

Type	Name	Description
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

Timeline

• Start date: 2018-07-05
• Primary completion: 2018-11-30
• Completion: 2019-08-31
• First posted: 2018-07-27
• Last updated: 2019-05-20

Locations

3 sites across 1 country: China