Simple, Mobile-based Artificial Intelligence AlgoRithms in the Detection of Diabetic ReTinopathy (SMART) Study
SMART
1 other identifier
observational
900
1 country
1
Brief Summary
This is an observational cross sectional study aimed to evaluate the performance of the artificial intelligence algorithm in detecting any grade of diabetic retinopathy using retinal images from patients with diabetes.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jul 2018
Shorter than P25 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
June 16, 2018
CompletedFirst Posted
Study publicly available on registry
June 28, 2018
CompletedStudy Start
First participant enrolled
July 11, 2018
CompletedPrimary Completion
Last participant's last visit for primary outcome
October 1, 2018
CompletedStudy Completion
Last participant's last visit for all outcomes
October 1, 2018
CompletedOctober 9, 2018
October 1, 2018
3 months
June 16, 2018
October 5, 2018
Conditions
Outcome Measures
Primary Outcomes (1)
Sensitivity and specificity of the AI in detecting any grade of diabetic retinopathy
3 months
Secondary Outcomes (2)
Sensitivity and specificity of the AI in detecting referable diabetic retinopathy (referable retinopathy defined as moderate non proliferative retinopathy or greater)
3 months
Sensitivity and specificity of the AI in detecting sight threatening diabetic retinopathy (referable retinopathy defined as severe non proliferative retinopathy or greater)
3 months
Interventions
This is an observational study of patients with diabetes. Patients undergoing routine care will undergo retinal imaging using a nonmydriatic fundus camera. The images will be run on an artificial intelligence (AI) algorithm. The diagnosis of the artificial intelligence algorithm will be compared to the image diagnosis given by the ophthalmologists. The ophthalmologists will be blinded to the diagnosis of the AI and vice versa. The data will be analyzed to evaluate the performance of the AI.
Eligibility Criteria
Individuals with diabetes mellitus presenting to the outpatient for routine clinical care.
You may qualify if:
- Patients with type 1 or type 2 diabetes mellitus
- Ages 18 and above
- Male and female
You may not qualify if:
- Persistent visual impairment in one or both eyes;
- Subjects with corneal opacities and advanced cataract.
- History of retinal vascular (vein or artery) occlusion;
- Subject is contraindicated for fundus photography (for example, has light sensitivity);
- Subject is currently enrolled in an interventional study of an investigational device or drug;
- Subject has a condition or is in a situation which in the opinion of the Investigator, might confound study results, may interfere significantly with the subject's participation in the study, or may result in ungradable clinical reference standard photographs.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Medios Technologies Pte. Ltdlead
- Diacon Hospitalcollaborator
Study Sites (1)
Diacon Hospital
Bangalore, 560010, India
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Srikanth Y N, MS
Investigator
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- CASE ONLY
- Time Perspective
- CROSS SECTIONAL
- Sponsor Type
- INDUSTRY
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Principal investigator
Study Record Dates
First Submitted
June 16, 2018
First Posted
June 28, 2018
Study Start
July 11, 2018
Primary Completion
October 1, 2018
Study Completion
October 1, 2018
Last Updated
October 9, 2018
Record last verified: 2018-10
Data Sharing
- IPD Sharing
- Will not share