Simple, Mobile-based Artificial Intelligence AlgoRithms in the Detection of Diabetic ReTinopathy (SMART) Study

Medios Technologies Pte. Ltd900 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

900

Conditions

Diabetic Retinopathy

Interventions

diabetic retinopathyOTHER

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

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. Patients with type 1 or type 2 diabetes mellitus 2. Ages 18 and above 3. Male and female Exclusion Criteria: 1. Persistent visual impairment in one or both eyes; 2. Subjects with corneal opacities and advanced cataract. 3. History of retinal vascular (vein or artery) occlusion; 4. Subject is contraindicated for fundus photography (for example, has light sensitivity); 5. Subject is currently enrolled in an interventional study of an investigational device or drug; 6. 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.

Locations (1)

Diacon Hospital

Bangalore, India

RECRUITING

Outcomes

Primary Outcomes

Sensitivity and specificity of the AI in detecting any grade of diabetic retinopathy

Time frame: 3 months

Secondary Outcomes

Sensitivity and specificity of the AI in detecting referable diabetic retinopathy (referable retinopathy defined as moderate non proliferative retinopathy or greater)

Time frame: 3 months

Sensitivity and specificity of the AI in detecting sight threatening diabetic retinopathy (referable retinopathy defined as severe non proliferative retinopathy or greater)

Time frame: 3 months

Central Contacts

Srikanth Y N, MS

CONTACT

9886450550 srikanthyn@yahoo.com

Bhavana Sosale, MD

CONTACT

9449478512 bhavanasosale@gmail.com

Data from ClinicalTrials.gov

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