Sensitivity and Specificity of Smartphone-Based Retinal Imaging for Diabetic Retinopathy

Sengupta, Sabyasachi; Sindal, Manavi D; Baskaran, Prabu; Pan, Utsab; Venkatesh, Rengaraj

doi:10.1016/j.oret.2018.09.016

Cited by 58 publications

(62 citation statements)

References 21 publications

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“…The Remidio FOP is an FDA510k cleared medical device validated in head-to-head studies against Topcon TRC 50DX and Zeiss FF450. It is the only smartphone-based device shown to have a high sensitivity and specificity in detection of all grades of DR, in non-mydriatic imaging 11 16. The results seen with the Medios AI using images captured from the Remidio FOP meet the FDA superiority cut-offs and are comparable to results observed with other AI algorithms, such as Google AI, EyeArt or IDx-DR for the detection of RDR 7 9 13.…”

Section: Discussionmentioning

confidence: 64%

Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study

Sosale

Aravind

Murthy³

et al. 2020

BMJ Open Diab Res Care

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IntroductionThe aim of this study is to evaluate the performance of the offline smart phone-based Medios artificial intelligence (AI) algorithm in the diagnosis of diabetic retinopathy (DR) using non-mydriatic (NM) retinal images.MethodsThis cross-sectional study prospectively enrolled 922 individuals with diabetes mellitus. NM retinal images (disc and macula centered) from each eye were captured using the Remidio NM fundus-on-phone (FOP) camera. The images were run offline and the diagnosis of the AI was recorded (DR present or absent). The diagnosis of the AI was compared with the image diagnosis of five retina specialists (majority diagnosis considered as ground truth).ResultsAnalysis included images from 900 individuals (252 had DR). For any DR, the sensitivity and specificity of the AI algorithm was found to be 83.3% (95% CI 80.9% to 85.7%) and 95.5% (95% CI 94.1% to 96.8%). The sensitivity and specificity of the AI algorithm in detecting referable DR (RDR) was 93% (95% CI 91.3% to 94.7%) and 92.5% (95% CI 90.8% to 94.2%).ConclusionThe Medios AI has a high sensitivity and specificity in the detection of RDR using NM retinal images.

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Section: Discussionmentioning

confidence: 64%

Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study

Sosale

Aravind

Murthy³

et al. 2020

BMJ Open Diab Res Care

View full text Add to dashboard Cite

show abstract

“…There is a substantial unmet need for accurate and simple-touse screening modalities that can be used globally to screen people for DR. Smartphone-based retinal imaging is emerging as a cost-effective way of screening for retinopathy in the community. 4,5 Similarly, automated analysis of retinal images captured using standard retinal cameras has the promise of being cost-effective and scalable within populationbased DR screening programs. 6,7 Incorporating similar automated analysis into low-cost, smartphone-based devices has been shown to be acceptable for screening.…”

mentioning

confidence: 99%

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

et al. 2019

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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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“…The Remidio Fundus on Phone camera system consists of a slit lamp-based nonmydriatic smartphone camera, which provides 45°images to detect DR and was validated against high-end fundus cameras, showing on-par performance for any DR and referable DR, thus having received FDA approval. 58,59 Automated Image Analysis: Before and After AI Telemedical assessment of CFP for DR screening requires a large amount of trained graders in reading centers to cope with the rising volume of images. For this and other reasons, the prospect of automated analysis of retinal images has garnered interest from ophthalmologists and computer scientists alike.…”

Section: Different Imaging Approachesmentioning

confidence: 99%

<p>The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence</p>

Huemer¹,

Wagner²,

Sim³

2020

OPTH

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As a third of people with diabetes mellitus (DM) will suffer the microvascular complications of diabetic retinopathy (DR) and therapeutic options can effectively prevent visual impairment, systematic screening has substantially reduced disease burden in developed countries. In an effort to tackle the rising incidence of DM, screening programmes have modernized in synchrony with technical and infrastructural advancements. Patient evaluation has shifted from face-to-face ophthalmologist-based review delivered through community grassroots to asynchronous store-and-forward modern telemedicine platforms commissioned on a nationwide scale. First pioneered with primitive 35-mm slide film retinal photography, the last decade has seen an emergence of high resolution and widefield imaging devices, which may reveal extents of DR indiscernible to the clinician but with implications of potential earlier identification. Similar progress has been seen in image analysis approachesautomated image analysis of retinal photographs of DR has evolved from qualitative feature detection to rules-based algorithms to autonomous artificial intelligence-powered classification. Such models have, relatively rapidly, been validated and are now receiving approval from health regulation authorities with deployment into the clinical sphere. In this review, we chart the evolution of global DR screening programmes since their inception highlighting major milestones in healthcare infrastructure, telemedicine approaches and imaging devices that have shaped the robust and effective frameworks recognised today. We also provide an outlook for the future of DR screening in the context of recent technological advancements with respect to their limitations in current times.

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Sensitivity and Specificity of Smartphone-Based Retinal Imaging for Diabetic Retinopathy

Cited by 58 publications

References 21 publications

Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study

Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study

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

<p>The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence</p>

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