Bangladesh PRODUCTIVity in Eyecare Trial

NCT ID: NCT05182580

Last Updated: 2024-02-06

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 993 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-03-20
• Study Completion Date: 2022-07-31

Brief 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.

Detailed Description

Bangladesh PRODUCTIVity in Eyecare (B-PRODUCTIVE) Trial

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

Hypothesis: Autonomous AI increases retina specialist productivity

Main Study Question: Will retina specialists complete a greater number of diabetic eye exams per working hour (including persons reviewed by AI whom the retina specialist does not need to see personally) when they use autonomous AI in a randomized clinical trial?

Design: Cluster-randomized (by clinic day) controlled trial.

Randomization: By clinic day. Each morning the clinic manager will open an opaque envelope, which informs the manager if it is an Intervention (AI) or Control (non-AI) day.

Interventions: All patients in both groups go through the eligibility checklist. If approved, they will be evaluated by autonomous AI. This is done to decrease potential bias (neither patients nor physicians know the group assignment of participants) and concealment (so that neither patients nor doctors can arrange visits on a known "Intervention Day").

Intervention Group: On randomly selected "Intervention" clinic days, if patients screen positive or have insufficient image quality, they continue to the ophthalmologist. If not eligible for autonomous AI, they proceed straight to the ophthalmologist without autonomous AI evaluation. If patients receive a negative result, they do not see the retina specialist, and are referred for a visit at the regular eye clinic (not the retina clinic) in 3 months.

Control Group: On randomly-selected "Control Days," all patients see the ophthalmologist, irrespective of the results of autonomous AI evaluation.

Masking: The retina doctors are masked both patient group assignment (that is, whether autonomous AI was used for pre-screening or not on the particular clinic day) and also masked to the results of the AI on Intervention days. Patients are also masked to group assignment and autonomous AI results.

Conditions

Diabetic Retinopathy; Diabetic Macular Edema

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: DOUBLE

Study Groups

Intervention Group

Autonomous AI results are used to evaluate if the participant needs to see the retina specialist (positive result) or not (negative result).

Group Type: EXPERIMENTAL

Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edema

Intervention Type: DIAGNOSTIC_TEST

If patients receive a negative result they do not see the retina specialist

Control Group

All participants see the retina specialist irrespective of the results of their autonomous AI evaluation.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edema

If patients receive a negative result they do not see the retina specialist

Intervention Type: DIAGNOSTIC_TEST

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

• Minimum Eligible Age: 22 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Digital Diagnostics, Inc.

INDUSTRY

Sponsor Role: collaborator

Deep Eye Care Foundation (DECF)

OTHER

Sponsor Role: collaborator

Orbis

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Nathan Congdon, MD, MPH

Role: STUDY_CHAIR

Orbis

Locations

Deep Eye Care Foundation

Rangpur City, , Bangladesh

Countries

Bangladesh

References

Abramoff MD, Whitestone N, Patnaik JL, Rich E, Ahmed M, Husain L, Hassan MY, Tanjil MSH, Weitzman D, Dai T, Wagner BD, Cherwek DH, Congdon N, Islam K. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. NPJ Digit Med. 2023 Oct 4;6(1):184. doi: 10.1038/s41746-023-00931-7.

Reference Type: DERIVED

PMID: 37794054 (View on PubMed)

Provided Documents

Document Type: Study Protocol and Statistical Analysis Plan

View Document

Other Identifiers

ORBIS-DXS-DECF-2021

Identifier Type: -

Identifier Source: org_study_id