Clinical trial NCT05324189: Automated AI-based System for Early Diagnosis of Diabetic Retinopathy
Official title: Pivotal Trial of Automated AI-based System for Early Diagnosis of Diabetic Retinopathy Using Retinal Color Imaging
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Diabetic Retinopathy
Lead sponsor: The New York Eye & Ear Infirmary
Sponsor class: OTHER
Collaborators: name: iHealthScreen Inc
First posted: 2022-04-12
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Participants who fit the eligibility inclusion criteria and not the exclusion criteria.

Brief summary: In this pivotal trial, we aim to perform a prospective study to find the efficacy of iPredict, an artificial intelligence (AI) based software tool on early diagnosis of Diabetic Retinopathy (DR)in the primary care, optometrist and other diabetes-screening clinics. DR is one of the leading causes of blindness in the United States and other developed countries. Every individual with diabetes is at risk of DR. It does not show any symptom until the disease is progressed to advanced stages. If the disease is caught at an early stage, it can be prevented, managed or treated effectively. Currently, screening for DR is done by the Ophthalmologists, which is limited to areas with limited availability. This is also time-consuming and expensive. All of these can be complemented by automated screening and set up the screening in the primary care clinics.

Detailed description: In this pivotal trial, we aim to invite diabetic patients to participate in the trial by having non-dilated photos of their eyes taken by an FDA-approved DRS plus camera at their own doctor's office which will test the feasibility of our proposed automated AI based DR diagnosis software solution,. The color fundus photos will be captured and then be transmitted securely and analyzed by iHealthScreen's HIPAA compliant server at Amazon cloud. The deep learning module will analyze the image for finding the disease severity. The automated report will be generated which will report as referable DR or more than mild (mtm) DR detected i.e., moderate DR, severe DR - proliferative or non-proliferative DR or Non-referable DR or mtm DR not detected, i.e., mild DR or no DR.

The same images will be evaluated by 3 ophthalmologists and will be adjudicated if any disagreement between the gradings. The automatic and expert evaluation will be compared to compute the sensitivity, specificity and AUC.

Eligibility criteria: Inclusion Criteria:

* Age of Subjects: Patients ≥ 18 years of age.
* Gender of Subjects: Both males and females will be invited to participate.
* Subjects with diabetes (A1C level 6.5 or higher) or Fasting Plasma Glucose (blood sugar level) 126 mg/dL (≥7.0 mmol/L)
* Subjects must be willing and are able to comply with clinic visit, understand the study-related procedures/provisions, and provide signed informed consent.
* asymptomatic patients with DR.

Exclusion Criteria:

* Subject has retinal degenerations and retinal vascular diseases such as age-related macular degeneration or having undergone prior retinal surgery.
* History of ocular injections,
* Subject has persistent visual impairment in any eye;
* History of macular edema or retinal vascular (vein or artery) occlusion;
* laser treatment of the retina, or intraocular surgery other than cataract surgery without complications;
* Subject is currently enrolled in an interventional study of an investigational device or drug;
* Subject has ungradable clinical reference standard photographs (i.e., not gradable quality image). If the patient image is not gradable automatically, we will suggest the patient to refer the ophthalmologist.

Primary outcomes:
measure: Sensitivity of identification of referable and non-referable Diabetic Retinopathy (DR) for early diagnosis of DR
timeFrame: 2 years
measure: Specificity of identification of referable and non-referable Diabetic
timeFrame: 2 years
Secondary outcomes:
measure: The accuracy of identification of referable and non-referable DR for early diagnosis of DR
timeFrame: 2 years
Other outcomes:
Locations:
city: New York
country: United States
facility: New York Eye and Ear Infirmary of Mount Sinai
state: New York
Study officials:
References: