Diabetic RetinopathyB-PRODUCTIVENCT05182580
Bangladesh PRODUCTIVity in Eyecare Trial
A study testing Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edema in people with Diabetic Retinopathy and Diabetic Macular Edema, run by Orbis at 1 site in Bangladesh.
Trial 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. Read more about this study
- Who is it for?
- Ages 22 years and olderHealthy volunteers can take part
- Conditions the study is for
- Diabetic Retinopathy, Diabetic Macular Edema
- What is being tested?
- Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edema
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 993 participants
- Where
- 1 siteBangladesh
- Study dates
- 20 Mar 2022to 31 Jul 2022
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 22 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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
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.
Read more
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.
Read the 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
- Being tested
- Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edema
- Medical subject headings
- Diabetic Retinopathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edemaDiagnostic test
If patients receive a negative result they do not see the retina specialist
| Study group | Role | What they receive |
|---|---|---|
| Intervention Group | Experimental | Autonomous AI results are used to evaluate if the participant needs to see the retina specialist (positive result) or not (negative result). Receives: Diagnostic Test: Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edema |
| Control Group | No intervention | All participants see the retina specialist irrespective of the results of their autonomous AI evaluation. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of Completed Care Encounters Among Clinic Patients With Diabetes Per Retina Specialist Clinic Hour
Number of completed care encounters among clinic patients with diabetes per retina specialist clinic hour. Numerator is the number of care encounters among patients with diabetes (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 clinic time in hours.
Measured: 105 randomized clinic days
Number of Completed Care Encounters Among All Clinic Patients (With and Without Diabetes) Per Retina Specialist Clinic Hour
Number of completed care encounters among all clinic patients (with and without diabetes) per retina specialist clinic hour. Numerator is the number of completed care encounters (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 clinic working time in hours.
Measured: 105 randomized clinic days
Secondary outcomes (2)
Specialist Productivity Adjusted for Patient Complexity for Patients With Diabetes
Specialist productivity (care encounters / specialist clinic hour) adjusted for patient complexity for patients with diabetes.
The complexity score for each patient participant was calculated by a masked United Kingdom National Health Service grader using the International Grading system, adapted from Wilkinson et al. International Clinical Diabetic Retinopathy and Diabetic Macular Edema Severity Scales (no DED = 0 points, mild non-proliferative DED = 0 points, moderate or severe non-proliferative DED = 1 point, proliferative DED = 3 points and diabetic macular edema = 2 points.) The patient participant complexity score was summed across both eyes. The average complexity score for each arm was calculated.
Complexity adjusted specialist productivity was calculated for intervention and control arms by multiplying the respective overall productivity (care encounters per specialist clinic hour) calculation by the respective average complexity score.
Measured: 105 randomized clinic days
Number of Participants Who Were Very Satisfied or Satisfied With Autonomous AI
After the patient participant completed the autonomous AI process, a survey with a 4-point Likert scale ("very satisfied," "satisfied," "dissatisfied," "very dissatisfied") was administered, concerning the participant's satisfaction with interactions with the healthcare team, time to receive examination results, and receiving their diagnosis from the autonomous AI system.
Measured: 105 randomized clinic days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jan 2022
- Study start20 Mar 2022
- Primary completion31 Jul 2022
- Study completion31 Jul 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Diabetic RetinopathyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Cluster-randomized (by clinic day) controlled trial.
- Masking
- Double blind
- Who is masked
- Participant, Care provider
- Enrolment
- 993 participants (actual)
Sponsor and investigators
- Lead sponsor
- Orbis
- Sponsor type
- University, hospital or other
- Collaborators
- Digital Diagnostics, Inc., Deep Eye Care Foundation (DECF)
- Responsible party
- Sponsor
- Investigators
- Nathan Congdon, MD, MPH, Study chair, Orbis
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05182580
- Sponsor's protocol ID
- ORBIS-DXS-DECF-2021
- Acronym
- B-PRODUCTIVE
- Status last verified
- Jan 2024
- First submitted
- 16 Nov 2021
- Last updated
- 6 Feb 2024
- Results posted
- Yes, on ClinicalTrials.gov
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No: We do not plan to share IPD
Publications
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.
PubMed 37794054
Study at a glance
From the registry record, last updated by the sponsor on 6 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT05182580
- Acronym / sponsor ID
- B-PRODUCTIVE / ORBIS-DXS-DECF-2021
- Conditions
- Diabetic Retinopathy, Diabetic Macular Edema
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 993 participants (actual)
- Lead sponsor
- OrbisUniversity, hospital or other
- Collaborators
- Digital Diagnostics, Inc., Deep Eye Care Foundation (DECF)
- Study start
- 20 Mar 2022 (actual)
- Primary completion
- 31 Jul 2022 (actual)
- Study completion
- 31 Jul 2022 (actual)
- First posted
- 10 Jan 2022 (actual)
- Results
- Posted 6 Feb 2024
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, care provider)
- Number of arms
- 2
- Model description
- Cluster-randomized (by clinic day) controlled trial.
| Arm | Type | Receives |
|---|---|---|
| Intervention Group | Experimental | Diagnostic Test: Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edemaAutonomous AI results are used to evaluate if the participant needs to see the retina specialist (positive result) or not (negative result). |
| Control Group | No intervention | –All participants see the retina specialist irrespective of the results of their autonomous AI evaluation. |
Who may qualify
- Ages
- 22 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edemaIf patients receive a negative result they do not see the retina specialist | Diagnostic test | Intervention Group |
Primary outcome
Number of Completed Care Encounters Among Clinic Patients With Diabetes Per Retina Specialist Clinic Hour
Time frame: 105 randomized clinic days
Number of completed care encounters among clinic patients with diabetes per retina specialist clinic hour. Numerator is the number of care encounters among patients with diabetes (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 clinic time in hours.
Number of Completed Care Encounters Among All Clinic Patients (With and Without Diabetes) Per Retina Specialist Clinic Hour
Time frame: 105 randomized clinic days
Number of completed care encounters among all clinic patients (with and without diabetes) per retina specialist clinic hour. Numerator is the number of completed care encounters (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 clinic working time in hours.
2 secondary outcomes
Specialist Productivity Adjusted for Patient Complexity for Patients With Diabetes
Time frame: 105 randomized clinic days
Number of Participants Who Were Very Satisfied or Satisfied With Autonomous AI
Time frame: 105 randomized clinic days
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Feb 2024.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Derived from this study: PubMed 37794054Abramoff 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.
- Individual participant dataSharing planned: No. We do not plan to share IPD
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Nathan Congdon, MD, MPH study chair
Orbis
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Results utilized from autonomous AI diagnostic system for diabetic retinopathy and/or diabetic macular edema in people with Diabetic Retinopathy and Diabetic Macular Edema, run by Orbis at 1 site in Bangladesh.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 6 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Bangladesh. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 6 Feb 2024). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of Completed Care Encounters Among Clinic Patients With Diabetes Per Retina Specialist Clinic Hour (time frame: 105 randomized clinic days). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05182580, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Feb 2024.