In this clinical trial, we plan to evaluate the usefulness of artificial intelligence (AI) software paired with a handheld retinal camera to compare diabetic retinopathy status in Bolivian patients as read by retina specialists versus the AI software.
Study Type
OBSERVATIONAL
Enrollment
1,034
Patient with diabetes will have their undilated and dilated images taken, with the images being centered on the macula and on the optic disc.
Centro Medico Kolping
Santa Cruz de la Sierra, Bolivia
Clinica Incor
Santa Cruz de la Sierra, Bolivia
Hospital Universitario Japones
Santa Cruz de la Sierra, Bolivia
Number of subjects where the image readout by retina specialists match the readouts by EyeStar AI system for identifying diabetic eye disease
To quantify image analysis performance of the EyeStar AI system paired with Pictor Plus cameras for the detection of referable Diabetic Retinopathy (DR), defined as severe non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), or diabetic macular edema (DME) against adjudicated reads of retinal images by retina specialists
Time frame: 3 months
Number of subjects with gradable images taken by the hand held camera
Pictures taken by the handheld retinal camera will be graded by the retina specialists for the quality of image for readouts
Time frame: 3 months
Number of subjects with ocular diseases other than diabetic retinopathy between the readout by the EyeStar AI system and those by the retina specialists
To assess the performance of the system in detection of other ocular diseases such as macular degeneration and glaucoma
Time frame: 3 months
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