Evaluation of an AI algorithm trained on an ethnically diverse dataset to screen a previously unseen population for diabetic retinopathy.

Purpose: This study aimed to determine the generalizability of an artificial intelligence (AI) algorithm trained on an ethnically diverse dataset to screen for referable diabetic retinopathy (RDR) in the Armenian population unseen during AI development.

Methods: This study comprised 550 patients with diabetes mellitus visiting the polyclinics of Armenia over 10 months requiring diabetic retinopathy (DR) screening. The Medios AI-DR algorithm was developed using a robust, diverse, ethnically balanced dataset with no inherent bias and deployed offline on a smartphone-based fundus camera.

A Novel, Smartphone-Based, Teleophthalmology-Enabled, Widefield Fundus Imaging Device With an Autocapture Algorithm.

Purpose: Widefield imaging can detect signs of retinal pathology extending beyond the posterior pole and is currently moving to the forefront of posterior segment imaging. We report a novel, smartphone-based, telemedicine-enabled, mydriatic, widefield retinal imaging device with autofocus and autocapture capabilities to be used by non-specialist operators.

Methods: The Remidio Vistaro uses an annular illumination design without cross-polarizers to eliminate Purkinje reflexes.

Design and Performance Characterization of a Novel, Smartphone-Based, Portable Digital Slit Lamp for Anterior Segment Screening Using Telemedicine.

Purpose: Telemedicine-enabled, portable digital slit lamps can help to decentralize screening to close-to-patient contexts. We report a novel design for a portable, digital slit lamp using a smartphone. It works on an advanced optical design and has the capability of instantaneous, objective photodocumentation to capture anterior segment images and is telemedicine-enabled.

A novel approach to anterior segment imaging with smartphones in the COVID-19 era.

Purpose: To report a novel, telemedicine-friendly, smartphone-based, wireless anterior segment device with instant photo-documentation ability in the COVID-19 era.

Methods: Anterior Imaging Module (AIM) was constructed based on a 50/50 beam splitter design, to match the magnification drum optics of slit-lamps with a three-step or higher level of magnification. The design fills the smartphone sensor fully at the lowest magnification and matches the fixed focus of the slit-lamp.

Insights into the growing popularity of artificial intelligence in ophthalmology.

Artificial intelligence (AI) in healthcare is the use of computer-algorithms in analyzing complex medical data to detect associations and provide diagnostic support outputs. AI and deep learning (DL) find obvious applications in fields like ophthalmology wherein huge amount of image-based data need to be analyzed; however, the outcomes related to image recognition are reasonably well-defined. AI and DL have found important roles in ophthalmology in early screening and detection of conditions such as diabetic retinopathy (DR), age-related macular degeneration (ARMD), retinopathy of prematurity (ROP), glaucoma, and other ocular disorders, being successful inroads as far as early screening and diagnosis are concerned and appear promising with advantages of high-screening accuracy, consistency, and scalability.