B-PRODUCTIVE: Bangladesh PRODUCTIVity in Eyecare Trial
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Intervention Group 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
If patients receive a negative result they do not see the retina specialist
|
| No Intervention: Control Group All participants see the retina specialist irrespective of the results of their autonomous AI evaluation. |
Outcome Measures
Primary Outcome Measures
- Number of examined participants with diabetes per retina specialist working hour [summed weekly from baseline through study completion, 1 year]
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.
- Number of examined retina participants per retina specialist working hour [summed weekly from baseline through study completion, 1 year]
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.
Secondary Outcome Measures
- Change in the number of DR treatments scheduled [from baseline through study completion, 1 year]
Change in number of patients with diabetic retinopathy per week scheduled for any treatment including injections, implants, laser or surgery.
- Change in complexity score [from baseline through study completion, 1 year]
• 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.
- Satisfaction with autonomous AI assessed by questionnaire. [through study completion, 1 year]
• 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).
- Number of participants willing to pay for testing by autonomous AI. [through study completion, 1 year]
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.
Eligibility Criteria
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
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Routinely provides laser treatment or intravitreal injections to >= 3 DR patients/month
Patients
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Diagnosed with type 1 or 2 diabetes
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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
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Deep Eye Care Foundation | Rangpur | Bangladesh |
Sponsors and Collaborators
- Orbis
- Digital Diagnostics Inc.
- Deep Eye Care Foundation (DECF)
Investigators
- Study Chair: Nathan Congdon, MD, MPH, Orbis
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- ORBIS-DXS-DECF-2021