Literature DB >> 28912515

Screening for vision-threatening diabetic retinopathy in South India: comparing portable non-mydriatic and standard fundus cameras and clinical exam.

S Sengupta1, M D Sindal1, C G Besirli2, S Upadhyaya1, R Venkatesh1, L M Niziol2, A L Robin2,3, M A Woodward2, P A Newman-Casey2.   

Abstract

PurposeTo evaluate the sensitivity and specificity of a portable non-mydriatic fundus camera to diagnose vision-threatening diabetic retinopathy (VTDR).Patients and methodsA prospective, single-site, comparative instrument validation study was undertaken at the Aravind Eye Care System. Overall, 155 subjects with and without diabetes were recruited. Images from 275 eyes were obtained with the (1) non-mydriatic Smartscope, (2) mydriatic Smartscope, and (3) mydriatic table-top camera of the macular, nasal, and superotemporal fields. A retina specialist performed a dilated fundus examination (DFE), (reference standard). Two masked retina specialists graded the images. Sensitivity and specificity to detect VTDR with the undilated Smartscope was calculated compared to DFE.ResultsGraders 1 and 2 had a sensitivity of 93% (95% confidence interval (CI): 87-97%) and 88% (95% CI: 81-93%) and a specificity of 84% (95% CI: 77-89%) and 90% (95% CI: 84-94%), respectively, in diagnosing VTDR with the undilated Smartscope compared to DFE. Compared with the dilated Topcon images, graders 1 and 2 had sensitivity of 88% (95% CI: 81-93%) and 82% (95% CI: 73-88%) and specificity of 99% (95% CI: 96-100%) and 99% (95% CI: 95-100%).ConclusionsRemote graders had high sensitivity and specificity in diagnosing VTDR with undilated Smartscope images, suggesting utility where portability is a necessity.

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Year:  2017        PMID: 28912515      PMCID: PMC5811716          DOI: 10.1038/eye.2017.199

Source DB:  PubMed          Journal:  Eye (Lond)        ISSN: 0950-222X            Impact factor:   3.775


  35 in total

1.  Understanding interobserver agreement: the kappa statistic.

Authors:  Anthony J Viera; Joanne M Garrett
Journal:  Fam Med       Date:  2005-05       Impact factor: 1.756

2.  Comparison Among Methods of Retinopathy Assessment (CAMRA) Study: Smartphone, Nonmydriatic, and Mydriatic Photography.

Authors:  Martha E Ryan; Ramachandran Rajalakshmi; Vijayaraghavan Prathiba; Ranjit Mohan Anjana; Harish Ranjani; K M Venkat Narayan; Timothy W Olsen; Viswanathan Mohan; Laura A Ward; Michael J Lynn; Andrew M Hendrick
Journal:  Ophthalmology       Date:  2015-07-16       Impact factor: 12.079

3.  Can we predict which patients are at risk of having an ungradeable digital image for screening for diabetic retinopathy?

Authors:  H Murgatroyd; A Cox; A Ellingford; J D Ellis; C J Macewen; G P Leese
Journal:  Eye (Lond)       Date:  2006-10-06       Impact factor: 3.775

4.  Peripheral lesions identified by mydriatic ultrawide field imaging: distribution and potential impact on diabetic retinopathy severity.

Authors:  Paolo S Silva; Jerry D Cavallerano; Jennifer K Sun; Ahmed Z Soliman; Lloyd M Aiello; Lloyd Paul Aiello
Journal:  Ophthalmology       Date:  2013-06-15       Impact factor: 12.079

5.  Screening for diabetic retinopathy: 1 and 3 nonmydriatic 45-degree digital fundus photographs vs 7 standard early treatment diabetic retinopathy study fields.

Authors:  Stela Vujosevic; Elisa Benetti; Francesca Massignan; Elisabetta Pilotto; Monica Varano; Fabiano Cavarzeran; Angelo Avogaro; Edoardo Midena
Journal:  Am J Ophthalmol       Date:  2009-05-05       Impact factor: 5.258

6.  Enhancing nonmydriatic color photographs of the retina with monochromatic views and a stereo pair to detect diabetic retinopathy.

Authors:  Haytham I Salti; Mona Nasrallah; Sandra Haddad; Walid Khairallah; I S Salti
Journal:  Ophthalmic Surg Lasers Imaging       Date:  2009 Jul-Aug

7.  Effectiveness and safety of screening for diabetic retinopathy with two nonmydriatic digital images compared with the seven standard stereoscopic photographic fields.

Authors:  Marie Carole Boucher; Jacques A Gresset; Karine Angioi; Sébastien Olivier
Journal:  Can J Ophthalmol       Date:  2003-12       Impact factor: 1.882

8.  Nonmydriatic digital retinal images for determining diabetic retinopathy.

Authors:  Jutalai Tanterdtham; Apichart Singalavanija; Chakrapong Namatra; Adisak Trinavarat; Nuttawut Rodanant; Parapun Bamroongsuk; Somanus Thoongsuwan; Wanna Euasobhon
Journal:  J Med Assoc Thai       Date:  2007-03

Review 9.  Current epidemiology of diabetic retinopathy and diabetic macular edema.

Authors:  Jie Ding; Tien Yin Wong
Journal:  Curr Diab Rep       Date:  2012-08       Impact factor: 4.810

Review 10.  Epidemiology of diabetic retinopathy and maculopathy in Africa: a systematic review.

Authors:  P I Burgess; I J C MacCormick; S P Harding; A Bastawrous; N A V Beare; P Garner
Journal:  Diabet Med       Date:  2013-04       Impact factor: 4.359

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  11 in total

1.  Accuracy and Reliability of a Handheld, Nonmydriatic Fundus Camera for the Remote Detection of Optic Disc Edema.

Authors:  Lulu Bursztyn; Maria A Woodward; Wayne T Cornblath; Hilary M Grabe; Jonathan D Trobe; Leslie Niziol; Lindsey B De Lott
Journal:  Telemed J E Health       Date:  2017-10-13       Impact factor: 3.536

2.  The diagnostic accuracy of an intelligent and automated fundus disease image assessment system with lesion quantitative function (SmartEye) in diabetic patients.

Authors:  Yi Xu; Yongyi Wang; Bin Liu; Lin Tang; Liangqing Lv; Xin Ke; Saiguang Ling; Lina Lu; Haidong Zou
Journal:  BMC Ophthalmol       Date:  2019-08-14       Impact factor: 2.209

Review 3.  Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review.

Authors:  Beau J Fenner; Raymond L M Wong; Wai-Ching Lam; Gavin S W Tan; Gemmy C M Cheung
Journal:  Ophthalmol Ther       Date:  2018-11-10

4.  Smartphone-Based, Rapid, Wide-Field Fundus Photography for Diagnosis of Pediatric Retinal Diseases.

Authors:  Tapan P Patel; Tyson N Kim; Gina Yu; Vaidehi S Dedania; Philip Lieu; Cynthia X Qian; Cagri G Besirli; Hakan Demirci; Todd Margolis; Daniel A Fletcher; Yannis M Paulus
Journal:  Transl Vis Sci Technol       Date:  2019-05-30       Impact factor: 3.283

5.  Effectiveness of Teleretinal Imaging-Based Hospital Referral Compared With Universal Referral in Identifying Diabetic Retinopathy: A Cluster Randomized Clinical Trial.

Authors:  Sanil Joseph; Ramasamy Kim; Ravilla D Ravindran; Astrid E Fletcher; Thulasiraj D Ravilla
Journal:  JAMA Ophthalmol       Date:  2019-07-01       Impact factor: 7.389

6.  Accuracy of the smartphone-based nonmydriatic retinal camera in the detection of sight-threatening diabetic retinopathy.

Authors:  Vijayaraghavan Prathiba; Ramachandran Rajalakshmi; Subramaniam Arulmalar; Manoharan Usha; Radhakrishnan Subhashini; Clare E Gilbert; Ranjit Mohan Anjana; Viswanathan Mohan
Journal:  Indian J Ophthalmol       Date:  2020-02       Impact factor: 1.848

7.  Retinal exams requested at Primary Care Unit: indications, results and alternative strategies of evaluation.

Authors:  Fernando Korn Malerbi; Adriano Biondi Monteiro Carneiro; Marcelo Katz; Claudio Luiz Lottenberg
Journal:  Einstein (Sao Paulo)       Date:  2019-09-16

8.  Effect of health education and screening location on compliance with diabetic retinopathy screening in a rural population in Maharashtra.

Authors:  Smita Singh; Ajay K Shukla; Azhar Sheikh; Girdharilal Gupta; Aarti More
Journal:  Indian J Ophthalmol       Date:  2020-02       Impact factor: 1.848

9.  Commentary: Targeted screening for effective detection of vision threatening diabetic retinopathy.

Authors:  Anantharaman Giridhar
Journal:  Indian J Ophthalmol       Date:  2021-11       Impact factor: 1.848

Review 10.  Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review.

Authors:  Margarita Labkovich; Megan Paul; Eliott Kim; Randal A Serafini; Shreyas Lakhtakia; Aly A Valliani; Andrew J Warburton; Aashay Patel; Davis Zhou; Bonnie Sklar; James Chelnis; Ebrahim Elahi
Journal:  Digit Health       Date:  2022-05-06
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