AI-Powered DIY Screening System for Diabetic Retinopathy

The Hong Kong Polytechnic University

Status: Enrolling

Conditions: Diabetic Retinopathy

Treatments: Diagnostic Test: AI-powered fundus camera screening

Study type: Interventional

Funder types: Other

Identifiers

NCT06892353

HSEARS20231218003

Details and patient eligibility

About

A pragmatic trial will be conducted in two representative clinics in each of the three types of targeted settings. It will be run for 3 months in each clinic to complete data collection of up to 100 patients in SPGC and Optometry Clinics, 200 at GPGC and 200 in GOPC in total. All the subjects will conduct a DIY screening, physician consultation, survey with questionnaires and a phone interview three months after their baseline assessment. This study will assess automated screening in terms of success rate of the DIY system without active assistant help, accuracy, screening rate and detection rate, adherence to referral and experience of participants, as well as cost-effectiveness in real-world settings.

Enrollment: 500 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

1. subjects who are 50 years of age or older, or subjects who are 18 years of age or older and have diabetes, and
2. have not undertaken an eye examination in the previous 12 months (elevated risk of undiagnosed disease).
3. Provide written informed consent.

Exclusion criteria

1. individuals with physical disabilities that prevent the use of a fundus camera, or
2. individuals with speech impairments who are unable to complete telephone follow-ups.

Trial design

Primary purpose: Screening

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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500 participants in 1 patient group

Screening

Experimental group

Description:

All the participants will undertake an AI-powered fundus camera screening, physician/optometrist consultation, survey with questionnaires and a phone interview three months after their baseline assessment.

Treatment:

Diagnostic Test: AI-powered fundus camera screening