Advancing AMD screening with an offline, AI-powered smartphone-based fundus camera: A prospective, real-world clinical validation.

Objectives: This study evaluated a novel offline, AI-driven age-related macular degeneration (AMD) screening algorithm against fundus image-only grading and the standard of care (combined Spectral Domain-Optical Coherence Tomography (SD-OCT) and fundus image grading).

Methods: Conducted prospectively at a South Asian tertiary eye hospital, this study utilized a validated smartphone-based non-mydriatic fundus camera to capture macula-centred images. The Medios AI's ability to detect referable AMD was compared to a reference standard image grading, using fundus images from the Zeiss Clarus 700 table-top camera and SD-OCT line scan across fovea.

Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages.

Purpose: Leveraging an artificial intelligence system (AI) for glaucoma screening can mitigate the current challenges and provide prompt detection and management crucial in averting irreversible blindness. The study reports the real-world performance of a glaucoma AI system deployed on a smartphone-based fundus camera across various severities of glaucoma.

Methods: In this prospective comparative study at a tertiary care glaucoma clinic, consecutive patients were evaluated by a glaucoma specialist using clinical assessment, visual field tests, and SD-OCT, and categorized as definite glaucoma, glaucoma suspect, or no glaucoma.

Diagnostic Performance of the Offline Medios Artificial Intelligence for Glaucoma Detection in a Rural Tele-Ophthalmology Setting.

Purpose: This study assesses the diagnostic efficacy of offline Medios Artificial Intelligence (AI) glaucoma software in a primary eye care setting, using nonmydriatic fundus images from Remidio's Fundus-on-Phone (FOP NM-10). Artificial intelligence results were compared with tele-ophthalmologists' diagnoses and with a glaucoma specialist's assessment for those participants referred to a tertiary eye care hospital.

Design: Prospective cross-sectional study PARTICIPANTS: Three hundred three participants from 6 satellite vision centers of a tertiary eye hospital.

Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera.

Objectives: Despite global research on early detection of age-related macular degeneration (AMD), not enough is being done for large-scale screening. Automated analysis of retinal images captured via smartphone presents a potential solution; however, to our knowledge, such an artificial intelligence (AI) system has not been evaluated. The study aimed to assess the performance of an AI algorithm in detecting referable AMD on images captured on a portable fundus camera.

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.

Evaluation of an offline, artificial intelligence system for referable glaucoma screening using a smartphone-based fundus camera: a prospective study.

Background/objectives: An affordable and scalable screening model is critical for undetected glaucoma. The study evaluated the performance of an offline, smartphone-based AI system for the detection of referable glaucoma against two benchmarks: specialist diagnosis following full glaucoma workup and consensus image grading.

Subjects/methods: This prospective study (tertiary glaucoma centre, India) included 243 subjects with varying severity of glaucoma and control group without glaucoma.

Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population.

Purpose: The primary objective of this study was to develop and validate an AI algorithm as a screening tool for the detection of retinopathy of prematurity (ROP).

Participants: Images were collected from infants enrolled in the KIDROP tele-ROP screening program.

Methods: We developed a deep learning (DL) algorithm with 227,326 wide-field images from multiple camera systems obtained from the KIDROP tele-ROP screening program in India over an 11-year period.

Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence.

Introduction: Numerous studies have demonstrated the use of artificial intelligence (AI) for early detection of referable diabetic retinopathy (RDR). A direct comparison of these multiple automated diabetic retinopathy (DR) image assessment softwares (ARIAs) is, however, challenging. We retrospectively compared the performance of two modern ARIAs, IDx-DR and Medios AI.

Agreement of a Novel Artificial Intelligence Software With Optical Coherence Tomography and Manual Grading of the Optic Disc in Glaucoma.

Prcis: The offline artificial intelligence (AI) on a smartphone-based fundus camera shows good agreement and correlation with the vertical cup-to-disc ratio (vCDR) from the spectral-domain optical coherence tomography (SD-OCT) and manual grading by experts.

Purpose: The purpose of this study is to assess the agreement of vCDR measured by a new AI software from optic disc images obtained using a validated smartphone-based imaging device, with SD-OCT vCDR measurements, and manual grading by experts on a stereoscopic fundus camera.

Methods: In a prospective, cross-sectional study, participants above 18 years (Glaucoma and normal) underwent a dilated fundus evaluation, followed by optic disc imaging including a 42-degree monoscopic disc-centered image (Remidio NM-FOP-10), a 30-degree stereoscopic disc-centered image (Kowa nonmyd WX-3D desktop fundus camera), and disc analysis (Cirrus SD-OCT).

Validation of a Simple-to-Use, Affordable, Portable Wavefront Aberrometry-Based Auto Refractometer in a Paediatric Population.

Purpose: InstaRef R20 is a handheld, affordable auto refractometer based on Shack Hartmann aberrometry technology. The study's objective was to compare InstaRef R20's performance for identifying refractive error in a paediatric population to that of standard subjective and objective refraction under both pre- and post-cycloplegic conditions.

Methods: Refraction was performed using 1) standard clinical procedure consisting of retinoscopy followed by subjective refraction (SR) under pre- and post-cycloplegic conditions and 2) InstaRef R20.

Validation of a simple-to-use, affordable, portable, wavefront aberrometry-based auto refractometer in the adult population: A prospective study.

Background: Refraction is one of the key components of a comprehensive eye examination. Auto refractometers that are reliable and affordable can be beneficial, especially in a low-resource community setting. The study aimed to validate the accuracy of a novel wave-front aberrometry-based auto refractometer, Instaref R20 against the open-field system and subjective refraction in an adult population.

Towards a Device Agnostic AI for Diabetic Retinopathy Screening: An External Validation Study.

Purpose: To evaluate the performance of a validated Artificial Intelligence (AI) algorithm developed for a smartphone-based camera on images captured using a standard desktop fundus camera to screen for diabetic retinopathy (DR).

Participants: Subjects with established diabetes mellitus.

Methods: Images captured on a desktop fundus camera (Topcon TRC-50DX, Japan) for a previous study with 135 consecutive patients (233 eyes) with established diabetes mellitus, with or without DR were analysed by the AI algorithm.

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.

Two-Year Outcomes of the New Low-cost Nonvalved Aurolab Aqueous Drainage Implant in Refractory Glaucoma.

Purpose: This study aimed to report the 2-year surgical outcomes in terms of efficacy and safety of the relatively new, indigenously manufactured, low-cost nonvalved drainage device Aurolab Aqueous Drainage Implant (AADI) in the management of refractory glaucomas in the Indian population.

Methods: A retrospective review of consecutive patients older than 12 years, who underwent nonvalved glaucoma drainage device surgery via AADI, was performed by a single fellowship-trained surgeon, between January 2014 and January 2018, who had at least 12 months of documented postoperative follow-up.

Outcome Measures: Primary-intraocular pressure (IOP).

Combined cataract extraction with a new nonvalved glaucoma drainage device in adult eyes with cataract and refractory glaucoma.

Purpose: The purpose of the study is to report the outcomes of simultaneous cataract extraction (CE) and a new nonvalved glaucoma drainage device (GDD), Aurolab Aqueous Drainage Implant (AADI), in eyes with cataract and refractory glaucoma.

Methods: This was a non-comparative, interventional, retrospective study. Consecutive patients who underwent AADI together with phacoemulsification from June-2015 to January-2017 by a single fellowship trained glaucoma surgeon with documented 3-months of follow-up were included.

Surgical outcomes of a new affordable non-valved glaucoma drainage device and Ahmed glaucoma valve: comparison in the first year.

Background: A new low-cost, indigenously manufactured, non-valved glaucoma drainage device (GDD) has been introduced and its design is based on the Baerveldt Glaucoma Device. We aim to demonstrate the efficacy and safety of this GDD (Aurolab aqueous drainage implant, AADI) vis-à-vis the valved device, Ahmed glaucoma valve (AGV), in the management of refractory glaucomas.

Design: Retrospective, comparative, interventional.

Surgical Outcomes of a New Low-Cost Nonvalved Glaucoma Drainage Device in Refractory Glaucoma: Results at 1 Year.

Purpose: The purpose of this study was to report the early outcomes in terms of efficacy and safety of a new, indigenously manufactured, low-cost nonvalved drainage device [AADI (Aurolab Aqueous Drainage Implant)] in the management of refractory glaucoma in the Indian population.

Methods: Retrospective review of consecutive patients older than 12 years, who underwent glaucoma drainage device surgery (GDD) using AADI, by a single fellowship-trained surgeon, between January 2014 and December 2016, who had at least 3 months of documented post-op follow-up.

Outcome Measures: Primary-intraocular pressure (IOP).

Outcomes of lensectomy and risk factors for failure in spherophakic eyes with secondary glaucoma.

Purpose: To report the outcomes of lensectomy in spherophakic eyes with subluxated or dislocated crystalline lenses and secondary glaucoma.

Methods: Lensectomy was performed in 52 eyes, 36 eyes with lens subluxation and 16 eyes with lens dislocation with secondary glaucoma from 1991 to 2016. Glaucoma was diagnosed based on intraocular pressure (IOP) ≥22 mm Hg and/or glaucomatous optic disc damage.