None listed
Conditions
Brief summary
Currently about 50% of patients living with diabetic retinopathy remain unaware they have the disease. Similarly, large amount of glaucoma and age-related macular degeneration cases remain undetected. To improve the detection rate. a novel system of automated fundus photography coupled with artificial intelligence (AI) will be evaluated in real world clinics to provide opportunistic screening for diabetic retinopathy, glaucoma, and age-related macular degeneration. The device is fully automated for image acquisition, analysis and report, which negates the need for clinical staffs. No prior training is required of the users. Within 2 to 3 minutes, participants can complete their own retinal photography and obtain a grading report based on the retinal photos for the risk of the above diseases. Referral recommendations are also included. This may benefit the patients in clinical settings where eye clinicians are in short supply. This study aims to test the performance of the AI screening kiosk in real world clinical settings. The AI screening results will be compared with the gold standard assessment by three eye clinicians. We hypothesize that this AI screening system is accurate, user friendly and can help improve the detection rate of some common eye diseases.
Interventions
An opportunisitc eye screening will be provided to all participants. In the waiting area of out-patient clinics, prior to seeing their non-eye physicians, eligible participants will be invited to perform opportunistic eye screening, using an automated retinal camera coupled with artificial intelligence (AI) technology. Participants who enrol in the study will perform retinal imaging on their own. With a flash illumination, fundus photo will be taken automatically without pupil dilatation. Based
An opportunisitc eye screening will be provided to all participants. In the waiting area of out-patient clinics, prior to seeing their non-eye physicians, eligible participants will be invited to perform opportunistic eye screening, using an automated retinal camera coupled with artificial intelligence (AI) technology. Participants who enrol in the study will perform retinal imaging on their own. With a flash illumination, fundus photo will be taken automatically without pupil dilatation. Based on the colour fundus images, the integrated AI technology will generate report on risk of glaucoma, diabetic retinopathy, and age-related macular degeneration, as well as referral recommendations. The system is operator-free and requires no prior training from participants. The automated voice instruction will guide the participants to complete the screening without help from staffs or technicians. This screening will take approximately 5 minutes to complete. No identifiable information will be entered or stored in the screening device. A QR code and unique study ID will be printed from the screening device and provided to the participants. By scanning the QR code, a full report can be available for review on participant's mobile phone, including the retinal images, the graded risk of the three eye diseases and referral recommendations. When referral to eye clinician is necessary, a referral letter will be provided by the endocrinology or general practice clinics. This multi-site study is expected to screen 500 participants in six months.
Sponsors
Centre for Eye Research Australia
Study design
Allocation
Non-randomised trial
Intervention model
Single group
Primary purpose
Diagnosis
Masking
Open (masking not used)
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. with diabetes and at least 18 years old 2. without diabetes and at least 50 years old
Exclusion criteria
None
Outcome results
None listed