Literature DB >> 31100178

Validation of Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening.

Yannick Bilong, Jean-Claude Katte, Godefroy Koki, Giles Kagmeni, Odile Pascale Nga Obama, Hermann Rossi Ngoufo Fofe, Caroline Mvilongo, Oliver Nkengfack, Andre Michel Bimbai, Eugene Sobngwi, Wilfred Mbacham, Jean Claude Mbanya, Lucienne Assumpta Bella, Ashish Sharma.   

Abstract

BACKROUND AND
OBJECTIVE: Screening for diabetic retinopathy (DR) is cost-effective when compared with disability loss for those who go blind in the absence of a screening program. We aimed to evaluate the sensitivity and specificity of a smartphone-based device for the screening and detection of DR. PATIENTS AND METHODS: A cross-sectional study of 220 patients with diabetes (440 eyes, all patients age 25 years or older) was completed. Tropicamide 0.5% was used for iris dilation followed by an indirect ophthalmoscopy using a 20-D lens. Retinal images were later obtained using a smartphone attached to an adaptable camera device. Retinal images permitted the visualization of the macular and papillary regions and were sent without compression via the internet to a retinal specialist for interpretation. Sensitivity and specificity were calculated for all cases and stages of DR.
RESULTS: Using our standard examination method, the prevalence of DR and macular edema were 13.6% and 6.4%, respectively. With the smartphone-based retinal camera, the prevalence of DR and macular edema were 18.2% and 8.2%, respectively. Sensitivity and specificity for the detection of all stages of DR was 73.3% and 90.5%, respectively. For the detection of macular edema, sensitivity was 77.8%, and specificity was 95%. For severe nonproliferative DR (NPDR), sensitivity and specificity were 80% and 99%, respectively; for proliferative DR (PDR), they were both 100%. In the early stages of DR, specificity was 89.8% for mild NPDR and 97.1% for moderate NPDR. Sensitivity was 57.1% and 42.9%, respectively.
CONCLUSION: Screening for DR using a smartphone-based retinal camera has a satisfactory specificity at all DR stages. Its sensitivity seems to be high only in the stages of DR necessitating a specific therapeutic decision (eg, macular edema, severe NPDR, and PDR). A smartphone-based retinal camera may be a useful device to screen for DR in resource-limited settings. [Ophthalmic Surg Lasers Imaging Retina. 2019;50:S18-S22.]. Copyright 2019, SLACK Incorporated.

Entities:  

Mesh:

Year:  2019        PMID: 31100178     DOI: 10.3928/23258160-20190108-05

Source DB:  PubMed          Journal:  Ophthalmic Surg Lasers Imaging Retina        ISSN: 2325-8160            Impact factor:   1.300


  8 in total

Review 1.  [Smartphone-based fundus imaging: applications and adapters].

Authors:  Linus G Jansen; Thomas Schultz; Frank G Holz; Robert P Finger; Maximilian W M Wintergerst
Journal:  Ophthalmologe       Date:  2021-12-16       Impact factor: 1.059

2.  Comparison of Telemedicine Screening of Diabetic Retinopathy by Mydriatic Smartphone-Based vs Nonmydriatic Tabletop Camera-Based Fundus Imaging.

Authors:  Yong Seok Han; Mythili Pathipati; Carolyn Pan; Loh-Shan Leung; Mark Scott Blumenkranz; David Myung; Brian Chiwing Toy
Journal:  J Vitreoretin Dis       Date:  2020-10-05

3.  Effectiveness of task-shifting for the detection of diabetic retinopathy in low- and middle-income countries: a rapid review protocol.

Authors:  Covadonga Bascaran; Nyawira Mwangi; Fabrizio D'Esposito; Iris Gordon; Juan Alberto Lopez Ulloa; Shaffi Mdala; Jacqueline Ramke; Jennifer R Evans; Matthew Burton
Journal:  Syst Rev       Date:  2021-01-04

Review 4.  Telemedicine in diabetic retinopathy screening in India.

Authors:  Kim Ramasamy; Chitaranjan Mishra; Naresh B Kannan; P Namperumalsamy; Sagnik Sen
Journal:  Indian J Ophthalmol       Date:  2021-11       Impact factor: 1.848

5.  Optimized hybrid machine learning approach for smartphone based diabetic retinopathy detection.

Authors:  Shubhi Gupta; Sanjeev Thakur; Ashutosh Gupta
Journal:  Multimed Tools Appl       Date:  2022-02-25       Impact factor: 2.577

6.  Validity of smartphone-based retinal photography (PEEK-retina) compared to the standard ophthalmic fundus camera in diagnosing diabetic retinopathy in Uganda: A cross-sectional study.

Authors:  Ahmed Mohamud Yusuf; Rebecca Claire Lusobya; John Mukisa; Charles Batte; Damalie Nakanjako; Otiti Juliet-Sengeri
Journal:  PLoS One       Date:  2022-09-06       Impact factor: 3.752

Review 7.  Digital innovations for retinal care in diabetic retinopathy.

Authors:  Stela Vujosevic; Celeste Limoli; Livio Luzi; Paolo Nucci
Journal:  Acta Diabetol       Date:  2022-08-12       Impact factor: 4.087

8.  Learning curve evaluation upskilling retinal imaging using smartphones.

Authors:  Linus G Jansen; Payal Shah; Bettina Wabbels; Frank G Holz; Robert P Finger; Maximilian W M Wintergerst
Journal:  Sci Rep       Date:  2021-06-16       Impact factor: 4.379

  8 in total

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