Bangladesh PRODUCTIVity in Eyecare Trial

Study Overview
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Brief Summary
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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
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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.

Official Title
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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

Conditions
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Diabetic Retinopathy, Diabetic Macular Edema

Intervention / Treatment
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* Diagnostic Test: Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edema


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 22 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: Cluster-randomized (by clinic day) controlled trial.
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Intervention Group<br>Autonomous AI results are used to evaluate if the participant needs to see the retina specialist (positive result) or not (negative result). | Diagnostic Test: Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edema<br>* If patients receive a negative result they do not see the retina specialist<br>|
| No Intervention: Control Group<br>All participants see the retina specialist irrespective of the results of their autonomous AI evaluation. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of examined participants with diabetes per retina specialist working hour | Number of examined participants with diabetes per retina specialist working hour. Numerator is the number of examined participants (including persons evaluated by autonomous AI on Intervention Days who are determined not to need to see the retina specialist). The denominator is retina specialist working time in hours. | summed weekly from baseline through study completion, 1 year | 
| Number of examined retina participants per retina specialist working hour | Number of examined retina participants per retina specialist working hour. Numerator is the number of examined participants (including persons evaluated by autonomous AI on Intervention Days who are determined not to need to see the retina specialist). The denominator is retina specialist working time in hours. | summed weekly from baseline through study completion, 1 year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in the number of DR treatments scheduled | Change in number of patients with diabetic retinopathy per week scheduled for any treatment including injections, implants, laser or surgery. | from baseline through study completion, 1 year | 
| Change in complexity score | • Change from baseline in mean complexity score of participants seen per hour per retina specialist. The complexity score is determined by external masked graders using a standard system adapted from Wilkinson et al. International Clinical Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) Severity Scales. The complexity score for an eye will be the sum of points with higher score as increased complexity (0=No or Mild Non-Proliferative DR (NPDR), 1 = Moderate NPDR or Severe NPDR, 3 = Proliferative DR (PDR), 2 = DME), and for a person it will be the sum of both eyes. | from baseline through study completion, 1 year | 
| Satisfaction with autonomous AI assessed by questionnaire. | • Retina specialist, technician and participant satisfaction with autonomous AI assessed by questionnaire using a 5 point Likert scale (1 = Very Satisfied 2 = Satisfied, 3 = Dissatisfied, 4 = Very Dissatisfied and 5 = N/A). | through study completion, 1 year | 
| Number of participants willing to pay for testing by autonomous AI. | Retina specialist and participant willingness to pay for testing by autonomous AI. Median amount willing to pay among those who would pay anything and percentage willing to pay anything. | through study completion, 1 year |