Assessing the Impact of Using Autonomous Artificial Intelligence (AI) for Pre-screening of Diabetic Retinopathy (DR) and Diabetic Macular Edema on Physician Productivity in Bangladesh
Trial ID: NCT05182580
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-03-20
Completion date: 2022-07-31
Sponsor: Orbis
Collaborators: Digital Diagnostics, Inc.; Deep Eye Care Foundation (DECF)
Conditions: Diabetic Retinopathy; Diabetic Macular Edema
Interventions: Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edema
Locations: Deep Eye Care Foundation
Countries: Bangladesh
Enrollment: 993
Age range: 22 Years - None
Sex: ALL

Summary:
The purpose of this study is to assess the impact of using autonomous artificial intelligence (AI) system for identification of diabetic retinopathy (DR) and diabetic macular edema on productivity of retina specialists in Bangladesh.

Globally, the number of people with diabetes mellitus is increasing. Diabetic retinopathy is a chronic, progressive complication of diabetes mellitus that affects the microvasculature of the retina, which if left untreated can potentially result in vision loss. Early detection and treatment of diabetic retinopathy can prevent potential blindness.

Study Aim: To assess the impact of using autonomous artificial intelligence (AI) system for detection of diabetic retinopathy (DR) and diabetic macular edema on physician productivity in Bangladesh.

Main study question: Will ophthalmologists with clinic days randomized to use autonomous AI DR detection for all persons with diabetes (diagnosed or un-diagnosed) visiting their clinic system have a greater number of examined patients with diabetes (by either AI or clinical exam), and a greater complexity of examined patients on a recognized grading scale, per physician working hour than those randomized not to have autonomous AI screening for their diabetes population?

The investigators anticipate that this study will demonstrate an increase in physician productivity, supporting efficiency for both physicians and patients, while also addressing increased access for DR screening; ultimately, preventing vision loss amongst diabetic patients. The study has the potential to contribute to the evidence base on the benefits of AI for physicians and patients. Additionally, the study has the potential to demonstrate the benefits (and/or challenges) of implementing AI in resource-constrained settings, such as Bangladesh.

Eligibility criteria:
Inclusion Criteria:

Retina specialists regularly seeing patients with DR

* Routinely examines \>= 20 patients with diabetes without known diabetic retinopathy or diabetic macular edema per week
* Routinely provides laser treatment or intravitreal injections to \>= 3 DR patients/month

Patients

* Diagnosed with type 1 or 2 diabetes
* Presenting visual acuity \>= 6/18 best corrected visual acuity in the better-seeing eye

Exclusion Criteria:

Retina specialists

* Currently using an AI system integrated into their clinical care and/or inability to provide informed consent.

Patients

* Inability to provide informed consent or understand the study; persistent vision loss, blurred vision or floaters; previously diagnosed with diabetic retinopathy or diabetic macular edema; history of laser treatment of the retina or injections into either eye, or any history of retinal surgery; contraindicated for imaging by fundus imaging systems

Outcomes:
Number of Completed Care Encounters Among Clinic Patients With Diabetes Per Retina Specialist Clinic Hour; Number of Completed Care Encounters Among All Clinic Patients (With and Without Diabetes) Per Retina Specialist Clinic Hour