N/A
Completed N=993
Bangladesh PRODUCTIVity in Eyecare Trial
Source: ClinicalTrials.gov NCT05182580 ↗Enrolled (actual)
993
Serious AEs
0.0%
Results posted
Feb 2024
Primary outcomePrimary: Number of Completed Care Encounters Among Clinic Patients With Diabetes Per Retina Specialist Clinic Hour — 1.59; 1.14 care encounters/specialist clinic hour
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.
Outcome Measures
| Outcome | Result | p-value |
|---|---|---|
| PRIMARY Number of Completed Care Encounters Among Clinic Patients With Diabetes Per Retina Specialist Clinic Hour |
1.59; 1.14 | — |
| PRIMARY Number of Completed Care Encounters Among All Clinic Patients (With and Without Diabetes) Per Retina Specialist Clinic Hour |
4.05; 3.36 | — |
| SECONDARY Specialist Productivity Adjusted for Patient Complexity for Patients With Diabetes |
3.15; 1.19 | — |
| SECONDARY Number of Participants Who Were Very Satisfied or Satisfied With Autonomous AI |
493; 499 | — |
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
Data sourced from ClinicalTrials.gov (NCT05182580). Outcome figures and adverse-event rates are extracted automatically from the registry's posted results and are provided for clinician reference, not as a substitute for the primary publication. Informational only — not medical advice.