Literature DB >> 33168977

Review of retinal cameras for global coverage of diabetic retinopathy screening.

Ramachandran Rajalakshmi1, Vijayaraghavan Prathiba2, Subramanian Arulmalar2, Manoharan Usha2.   

Abstract

The global burden of diabetes has resulted in an increase in the prevalence of diabetic retinopathy (DR), a microvascular complication of diabetes. Lifelong repetitive screening for DR is essential for early detection and timely management to prevent visual impairment due to the silent sight-threatening disorder. Colour fundus photography (CFP) is helpful for documentation of the retinopathy as well as for counselling the patient. CFP has established roles in DR screening, detection, progression and monitoring of treatment response. DR screening programmes use validated mydriatic or non-mydriatic fundus cameras for retinal imaging and trained image graders identify referable DR. Smartphone-based fundus cameras and handheld fundus cameras that are cost-effective, portable and easy to handle in remote places are gaining popularity in recent years. The images captured with these low-cost devices can be immediately sent to trained ophthalmologists for grading of DR. Recent increase in numbers of telemedicine programmes based on imaging with digital fundus cameras and remote interpretation has facilitated larger population coverage of DR screening and timely referral of those with sight-threatening DR to ophthalmologists. Good-quality retinal imaging and accurate diagnosis are essential to reduce inappropriate referrals. Advances in digital imaging such as ultra-wide field imaging and multi-modal imaging have opened new avenues for assessing DR. Fundus cameras with integrated artificial intelligence (AI)-based automated algorithms can also provide instant DR diagnosis and reduce the burden of healthcare systems. We review the different types of fundus cameras currently used in DR screening and management around the world.

Entities:  

Mesh:

Year:  2020        PMID: 33168977      PMCID: PMC7852572          DOI: 10.1038/s41433-020-01262-7

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


  47 in total

Review 1.  Update on Screening for Sight-Threatening Diabetic Retinopathy.

Authors:  Peter H Scanlon
Journal:  Ophthalmic Res       Date:  2019-05-27       Impact factor: 2.892

Review 2.  Some aspects of the organization of the output of the motor cortex.

Authors:  H G Kuypers
Journal:  Ciba Found Symp       Date:  1987

Review 3.  Use of Telemedicine Technologies in Diabetes Prevention and Control in Resource-Constrained Settings: Lessons Learned from Emerging Economies.

Authors:  Rajendra Pradeepa; Ramachandran Rajalakshmi; Viswanathan Mohan
Journal:  Diabetes Technol Ther       Date:  2019-06       Impact factor: 6.118

4.  Comparison of two reference standards in validating two field mydriatic digital photography as a method of screening for diabetic retinopathy.

Authors:  P H Scanlon; R Malhotra; R H Greenwood; S J Aldington; C Foy; M Flatman; S Downes
Journal:  Br J Ophthalmol       Date:  2003-10       Impact factor: 4.638

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.  Screening of diabetic retinopathy: effect of field number and mydriasis on sensitivity and specificity of digital fundus photography.

Authors:  F Aptel; P Denis; F Rouberol; C Thivolet
Journal:  Diabetes Metab       Date:  2008-04-10       Impact factor: 6.041

Review 7.  Retinal Imaging Techniques for Diabetic Retinopathy Screening.

Authors:  James Kang Hao Goh; Carol Y Cheung; Shaun Sebastian Sim; Pok Chien Tan; Gavin Siew Wei Tan; Tien Yin Wong
Journal:  J Diabetes Sci Technol       Date:  2016-02-01

Review 8.  Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis.

Authors:  Seth R Flaxman; Rupert R A Bourne; Serge Resnikoff; Peter Ackland; Tasanee Braithwaite; Maria V Cicinelli; Aditi Das; Jost B Jonas; Jill Keeffe; John H Kempen; Janet Leasher; Hans Limburg; Kovin Naidoo; Konrad Pesudovs; Alex Silvester; Gretchen A Stevens; Nina Tahhan; Tien Y Wong; Hugh R Taylor
Journal:  Lancet Glob Health       Date:  2017-10-11       Impact factor: 26.763

9.  Systematic review and meta-analysis of diagnostic accuracy of detection of any level of diabetic retinopathy using digital retinal imaging.

Authors:  Mapa Mudiyanselage Prabhath Nishantha Piyasena; Gudlavalleti Venkata S Murthy; Jennifer L Y Yip; Clare Gilbert; Tunde Peto; Iris Gordon; Suwin Hewage; Sureshkumar Kamalakannan
Journal:  Syst Rev       Date:  2018-11-07

Review 10.  Insights into the growing popularity of artificial intelligence in ophthalmology.

Authors:  Sreetama Dutt; Anand Sivaraman; Florian Savoy; Ramachandran Rajalakshmi
Journal:  Indian J Ophthalmol       Date:  2020-07       Impact factor: 1.848

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  11 in total

1.  Feasibility of telemedicine program using a hand-held nonmydriatic retinal camera in Panama.

Authors:  Alexander S Himstead; Janani Prasad; Sean Melucci; Kevin M Gustafson; Paul E Israelsen; Andrew Browne
Journal:  Int J Ophthalmol       Date:  2022-06-18       Impact factor: 1.645

2.  Optics and Utility of Low-Cost Smartphone-Based Portable Digital Fundus Camera System for Screening of Retinal Diseases.

Authors:  K V Chalam; Joud Chamchikh; Suzie Gasparian
Journal:  Diagnostics (Basel)       Date:  2022-06-20

3.  Tele-Ophthalmology Versus Face-to-Face Retinal Consultation for Assessment of Diabetic Retinopathy in Diabetes Care Centers in India: A Multicenter Cross-Sectional Study.

Authors:  Ramachandran Rajalakshmi; Ganesan UmaSankari; Vijayaraghavan Prathiba; Ranjit Mohan Anjana; Ranjit Unnikrishnan; Ulagamathesan Venkatesan; Saravanan JebaRani; Coimbatore Subramanian Shanthirani; Sobha Sivaprasad; Viswanathan Mohan
Journal:  Diabetes Technol Ther       Date:  2022-04-13       Impact factor: 7.337

4.  Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology.

Authors:  Yang Xiang; Lai Shujin; Chang Hongfang; Wen Yinping; Yu Dawei; Dong Zhou; Li Zhiqing
Journal:  Biomed Res Int       Date:  2021-04-20       Impact factor: 3.411

Review 5.  Various models for diabetic retinopathy screening that can be applied to India.

Authors:  Ramachandran Rajalakshmi; Vijayaraghavan Prathiba; Padmaja Kumari Rani; Viswanathan Mohan
Journal:  Indian J Ophthalmol       Date:  2021-11       Impact factor: 1.848

6.  Knowledge, attitude, and practice patterns and the purported reasons for delayed presentation of patients with sight-threatening diabetic retinopathy at a tertiary eyecare facility in Central India: A questionnaire-based study.

Authors:  Alok Sen; Parul Pathak; Pratik Shenoy; Gaurav Mohan Kohli; Priyavrat Bhatia; Sachin Shetty
Journal:  Indian J Ophthalmol       Date:  2021-11       Impact factor: 1.848

7.  Commentary: Utility of a smartphone-assisted direct ophthalmoscope camera for a general practitioner in screening of diabetic retinopathy at a primary health care center.

Authors:  Ashish Markan; Simar R Singh; Mohit Dogra
Journal:  Indian J Ophthalmol       Date:  2021-11       Impact factor: 1.848

8.  Utility of a smartphone assisted direct ophthalmoscope camera for a general practitioner in screening of diabetic retinopathy at a primary health care center.

Authors:  Dhaivat Shah; Lubhavni Dewan; Anukruti Singh; Deepika Jain; Tina Damani; Rinal Pandit; Amit Champalal Porwal; Sanjay Bhatnagar; Meghna Shrishrimal; Abhishek Patel
Journal:  Indian J Ophthalmol       Date:  2021-11       Impact factor: 1.848

9.  In-Person Verification of Deep Learning Algorithm for Diabetic Retinopathy Screening Using Different Techniques Across Fundus Image Devices.

Authors:  Nida Wongchaisuwat; Adisak Trinavarat; Nuttawut Rodanant; Somanus Thoongsuwan; Nopasak Phasukkijwatana; Supalert Prakhunhungsit; Lukana Preechasuk; Papis Wongchaisuwat
Journal:  Transl Vis Sci Technol       Date:  2021-11-01       Impact factor: 3.283

10.  Cost-effectiveness of artificial intelligence screening for diabetic retinopathy in rural China.

Authors:  Xiao-Mei Huang; Bo-Fan Yang; Wen-Lin Zheng; Qun Liu; Fan Xiao; Pei-Wen Ouyang; Mei-Jun Li; Xiu-Yun Li; Jing Meng; Tian-Tian Zhang; Yu-Hong Cui; Hong-Wei Pan
Journal:  BMC Health Serv Res       Date:  2022-02-25       Impact factor: 2.655

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