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

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          Abstract

          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. The measured resolution using the US Air Force target test was 64 line pairs (lp)/mm in the center, 57 lp/mm in the middle, and 45 lp/mm in the periphery of a single-shot retinal image. An autocapture algorithm was developed to capture images automatically upon reaching the correct working distance. The field of view (FOV) was validated using both model and real eyes. A pilot study was conducted to objectively assess image quality. The FOVs of montaged images from the Vistaro were compared with regulatory-approved widefield and ultra-widefield devices.

          Results

          The FOV of the Vistaro was found to be approximately 65° in one shot. Automatic image capture was achieved in 80% of patient examinations within an average of 10 to 15 seconds. Consensus grading of image quality among three graders showed that 91.6% of the images were clinically useful. A two-field montage on the Vistaro was shown to exceed the cumulative FOV of a seven-field Early Treatment Diabetic Retinopathy Study image.

          Conclusions

          A novel, smartphone-based, portable, mydriatic, widefield imaging device can view the retina beyond the posterior pole with a FOV of 65° in one shot.

          Translational Relevance

          Smartphone-based widefield imaging can be widely used to screen for retinal pathologies beyond the posterior pole.

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          Most cited references35

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          Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone

          Question What is the performance of an offline, automated artificial intelligence system of analysis to detect referable diabetic retinopathy on images taken by a health worker on a smartphone-based, nonmydriatic retinal camera? Finding In this cross-sectional study, fundus images from 213 study participants were subjected to offline, automated analysis. The sensitivity and specificity of the analysis to diagnose referable diabetic retinopathy were 100.0% and 88.4%, respectively, and the sensitivity and specificity for any diabetic retinopathy were 85.2% and 92.0%, respectively. Meaning This study suggests these methods might be used to screen for referable diabetic retinopathy using offline artificial intelligence and a smartphone-based, nonmydriatic retinal imaging system. This cross-sectional study compares the diagnostic accuracy of a smartphone-based artificial intelligence system vs ophthalmologist judgement in patients with referable diabetic retinopathy or any diabetic retinopathy in Mumbai, India. Importance Offline automated analysis of retinal images on a smartphone may be a cost-effective and scalable method of screening for diabetic retinopathy; however, to our knowledge, assessment of such an artificial intelligence (AI) system is lacking. Objective To evaluate the performance of Medios AI (Remidio), a proprietary, offline, smartphone-based, automated system of analysis of retinal images, to detect referable diabetic retinopathy (RDR) in images taken by a minimally trained health care worker with Remidio Non-Mydriatic Fundus on Phone, a smartphone-based, nonmydriatic retinal camera. Referable diabetic retinopathy is defined as any retinopathy more severe than mild diabetic retinopathy, with or without diabetic macular edema. Design, Setting, and Participants This prospective, cross-sectional, population-based study took place from August 2018 to September 2018. Patients with diabetes mellitus who visited various dispensaries administered by the Municipal Corporation of Greater Mumbai in Mumbai, India, on a particular day were included. Interventions Three fields of the fundus (the posterior pole, nasal, and temporal fields) were photographed. The images were analyzed by an ophthalmologist and the AI system. Main Outcomes and Measures To evaluate the sensitivity and specificity of the offline automated analysis system in detecting referable diabetic retinopathy on images taken on the smartphone-based, nonmydriatic retinal imaging system by a health worker. Results Of 255 patients seen in the dispensaries, 231 patients (90.6%) consented to diabetic retinopathy screening. The major reasons for not participating were unwillingness to wait for screening and the blurring of vision that would occur after dilation. Images from 18 patients were deemed ungradable by the ophthalmologist and hence were excluded. In the remaining participants (110 female patients [51.6%] and 103 male patients [48.4%]; mean [SD] age, 53.1 [10.3] years), the sensitivity and specificity of the offline AI system in diagnosing referable diabetic retinopathy were 100.0% (95% CI, 78.2%-100.0%) and 88.4% (95% CI, 83.2%-92.5%), respectively, and in diagnosing any diabetic retinopathy were 85.2% (95% CI, 66.3%-95.8%) and 92.0% (95% CI, 97.1%-95.4%), respectively, compared with ophthalmologist grading using the same images. Conclusions and Relevance These pilot study results show promise in the use of an offline AI system in community screening for referable diabetic retinopathy with a smartphone-based fundus camera. The use of AI would enable screening for referable diabetic retinopathy in remote areas where services of an ophthalmologist are unavailable. This study was done on patients with diabetes who were visiting a dispensary that provides curative services to the population at the primary level. A study with a larger sample size may be needed to extend the results to general population screening, however.
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            A mobile phone-based retinal camera for portable wide field imaging.

            Digital fundus imaging is used extensively in the diagnosis, monitoring and management of many retinal diseases. Access to fundus photography is often limited by patient morbidity, high equipment cost and shortage of trained personnel. Advancements in telemedicine methods and the development of portable fundus cameras have increased the accessibility of retinal imaging, but most of these approaches rely on separate computers for viewing and transmission of fundus images. We describe a novel portable handheld smartphone-based retinal camera capable of capturing high-quality, wide field fundus images. The use of the mobile phone platform creates a fully embedded system capable of acquisition, storage and analysis of fundus images that can be directly transmitted from the phone via the wireless telecommunication system for remote evaluation.
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              Is Open Access

              The clinical relevance of visualising the peripheral retina

              Recent developments in imaging technologies now allow the documentation, qualitative and quantitative evaluation of peripheral retinal lesions. As wide field retinal imaging, capturing both the central and peripheral retina up to 200° eccentricity, is becoming readily available the question is: what is it that we gain by imaging the periphery? Based on accumulating evidence it is clear that findings in the periphery do not always associate to those observed in the posterior pole. However, the newly acquired information may provide useful clues to previously unrecognised disease features and may facilitate more accurate disease prognostication. In this review, we explore the anatomy and physiology of the peripheral retina, focusing on how it differs from the posterior pole, recount the history of peripheral retinal imaging, describe various peripheral retinal lesions and evaluate the overall relevance of peripheral retinal findings to different diseases.
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                Author and article information

                Journal
                Transl Vis Sci Technol
                Transl Vis Sci Technol
                tvst
                TVST
                Translational Vision Science & Technology
                The Association for Research in Vision and Ophthalmology
                2164-2591
                18 October 2021
                October 2021
                : 10
                : 12
                : 21
                Affiliations
                [1 ]Research & Development, Remidio Innovative Solutions Pvt. Ltd., Bangalore, Karnataka, India
                [2 ]Department of Eye and Retinal Diseases, Diacon Hospital, Bangalore, Karnataka, India
                Author notes
                Correspondence: Anand Sivaraman, Research & Development, Remidio Innovative Solutions Pvt. Ltd., No. 1-51-2/12, II Floor, Vacuum Techniques Compound, 1st Cross Road, Phase-I, Peenya, Bangalore, Karnataka 560058, India. e-mail: anand@ 123456remidio.com
                Article
                TVST-21-3770
                10.1167/tvst.10.12.21
                8525841
                34661624
                14b2aa12-2fd1-4021-9d53-2bb958a031ee
                Copyright 2021 The Authors

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 06 August 2021
                : 07 June 2021
                Page count
                Pages: 16
                Categories
                Methods
                Methods

                widefield imaging,fundus,retina,field of view,autofocus,autocapture,smartphone,screening

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