Diabetic Retinopathy Screening Ratio Is Improved When Using a Digital, Nonmydriatic Fundus Camera Onsite in a Diabetes Outpatient Clinic

Roser, Pia; Kalscheuer, Hannes; Groener, Jan B.; Lehnhoff, D; Klein, Roman; Auffarth, Gerd U.; Nawroth, Peter P.; Schuett, Florian; Rudofsky, Gottfried

doi:10.1155/2016/4101890

Cited by 14 publications

(6 citation statements)

References 45 publications

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“…Considering the higher prevalence of cataracts in low-income country settings [ 29 , 36 ], we made the presence of cataract an exclusion criterion in our sample selection and chose to deploy a mydriatic approach for imaging. Although the gold standard for diabetic retinopathy screening is a 7-field photograph using a nonmydriatic approach [ 37 ], the applicability of this technique in low-income country settings is highly controversial [ 38 ]. When using a nonmydriatic approach, the body’s autonomic nervous system becomes hyperactive with reflex pupillary constriction in the second eye after taking an image of the first eye [ 39 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of Detecting Diabetic Retinopathy by Using Digital Fundus Photographs in the Peripheral Health Facilities of Bangladesh: Validation Study

Begum

Rahman

Nomani

et al. 2021

JMIR Public Health Surveill

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Background Diabetic retinopathy can cause blindness even in the absence of symptoms. Although routine eye screening remains the mainstay of diabetic retinopathy treatment and it can prevent 95% of blindness, this screening is not available in many low- and middle-income countries even though these countries contribute to 75% of the global diabetic retinopathy burden. Objective The aim of this study was to assess the diagnostic accuracy of diabetic retinopathy screening done by non-ophthalmologists using 2 different digital fundus cameras and to assess the risk factors for the occurrence of diabetic retinopathy. Methods This validation study was conducted in 6 peripheral health facilities in Bangladesh from July 2017 to June 2018. A double-blinded diagnostic approach was used to test the accuracy of the diabetic retinopathy screening done by non-ophthalmologists against the gold standard diagnosis by ophthalmology-trained eye consultants. Retinal images were taken by using either a desk-based camera or a hand-held camera following pupil dilatation. Test accuracy was assessed using measures of sensitivity, specificity, and positive and negative predictive values. Overall agreement with the gold standard test was reported using the Cohen kappa statistic (κ) and area under the receiver operating curve (AUROC). Risk factors for diabetic retinopathy occurrence were assessed using binary logistic regression. Results In 1455 patients with diabetes, the overall sensitivity to detect any form of diabetic retinopathy by non-ophthalmologists was 86.6% (483/558, 95% CI 83.5%-89.3%) and the specificity was 78.6% (705/897, 95% CI 75.8%-81.2%). The accuracy of the correct classification was excellent with a desk-based camera (AUROC 0.901, 95% CI 0.88-0.92) and fair with a hand-held camera (AUROC 0.710, 95% CI 0.67-0.74). Out of the 3 non-ophthalmologist categories, registered nurses and paramedics had strong agreement with kappa values of 0.70 and 0.85 in the diabetic retinopathy assessment, respectively, whereas the nonclinical trained staff had weak agreement (κ=0.35). The odds of having retinopathy increased with the duration of diabetes measured in 5-year intervals (P<.001); the odds of having retinopathy in patients with diabetes for 5-10 years (odds ratio [OR] 1.81, 95% CI 1.37-2.41) and more than 10 years (OR 3.88, 95% CI 2.91-5.15) were greater than that in patients with diabetes for less than 5 years. Obesity was found to have a negative association (P=.04) with diabetic retinopathy. Conclusions Digital fundus photography is an effective screening tool with acceptable diagnostic accuracy. Our findings suggest that diabetic retinopathy screening can be accurately performed by health care personnel other than eye consultants. People with more than 5 years of diabetes should receive priority in any community-level retinopathy screening program. In a country like Bangladesh where no diabetic retinopathy screening services exist, the use of hand-held cameras can be considered as a cost-effective option for potential system-wide implementation.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Accuracy of Detecting Diabetic Retinopathy by Using Digital Fundus Photographs in the Peripheral Health Facilities of Bangladesh: Validation Study

Begum

Rahman

Nomani

et al. 2021

JMIR Public Health Surveill

View full text Add to dashboard Cite

show abstract

“…Digital teleophthalmology systems that use nonmydriatic cameras have been implemented to improve DR screening rates and are cost-effective . Digital fundus photography can be efficiently and safely performed without pupil dilation, including in the pediatric setting . Recently, the US Food and Drug Administration (FDA) approved the first autonomous artificial intelligence (AI) diagnostic system to detect DR in adults.…”

Section: Introductionmentioning

confidence: 99%

Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes

Wolf¹,

Channa

Abràmoff

et al. 2020

JAMA Ophthalmol

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ouths and adults with diabetes are at risk for diabetic retinopathy (DR), which can lead to vision loss. 1 The prevalence of DR among youths with type 1 diabetes (T1D) and type 2 diabetes (T2D) ranges from 4% to 13%. [2][3][4] Despite recommendations from the American Diabetes Association and American Academy of Ophthalmology for yearly screening, adherence remains low. 5 Although the prevalence is low among youths, the risk of developing DR is high. Diabetic retinopathy is present in up to 50% of patients with T1D 28 years or more after diagnosis 6,7 and already present at the time of diagnosis in 12% to 19% of patients with T2D. 4 Digital teleophthalmology systems that use nonmydriatic cameras have been implemented to improve DR screen-ing rates 8,9 and are cost-effective. 10 Digital fundus photography can be efficiently and safely performed without pupil dilation, including in the pediatric setting. 3,[11][12][13][14] Recently, the US Food and Drug Administration (FDA) approved the first autonomous artificial intelligence (AI) diagnostic system to detect DR in adults. With this system, a minimally trained operator guided by an image-quality AI takes retinal images with a nonmydriatic fundus camera, and these images are subsequently assessed in real time at the point of care (POC) for the presence or absence of DR. 15 In 2020, this form of FDA-validated autonomous AI became part of the American Diabetes Association standard of care for DR screening. 16 IMPORTANCE Screening for diabetic retinopathy is recommended for children with type 1 diabetes (T1D) and type 2 diabetes (T2D), yet screening rates remain low. Point-of-care diabetic retinopathy screening using autonomous artificial intelligence (AI) has become available, providing immediate results in the clinic setting, but the cost-effectiveness of this strategy compared with standard examination is unknown.OBJECTIVE To assess the cost-effectiveness of detecting and treating diabetic retinopathy and its sequelae among children with T1D and T2D using AI diabetic retinopathy screening vs standard screening by an eye care professional (ECP). DESIGN, SETTING, AND PARTICIPANTSIn this economic evaluation, parameter estimates were obtained from the literature from 1994 to 2019 and assessed from March 2019 to January 2020. Parameters included out-of-pocket cost for autonomous AI screening, ophthalmology visits, and treating diabetic retinopathy; probability of undergoing standard retinal examination; relative odds of undergoing screening; and sensitivity, specificity, and diagnosability of the ECP screening examination and autonomous AI screening. MAIN OUTCOMES AND MEASURESCosts or savings to the patient based on mean patient payment for diabetic retinopathy screening examination and cost-effectiveness based on costs or savings associated with the number of true-positive results identified by diabetic retinopathy screening. RESULTSIn this study, the expected true-positive proportions for standard ophthalmologic screening by an ECP were 0.006 for T1D and 0.01 for...

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“…To improve accessibility and screening rates, digital fundus photography using nonmydriatic cameras has been implemented in adult and pediatric clinics (10)(11)(12). Digital fundus photography can be performed without the need for pupil dilation and takes a few minutes compared with traditional dilated eye exams (13,14).…”

mentioning

confidence: 99%

The SEE Study: Safety, Efficacy, and Equity of Implementing Autonomous Artificial Intelligence for Diagnosing Diabetic Retinopathy in Youth

Wolf¹,

Liu²,

Thomas³

et al. 2021

Diabetes Care

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Diabetic retinopathy (DR) is a leading cause of vision loss worldwide. Screening for DR is recommended in children and adolescents, but adherence is poor. Recently, autonomous artificial intelligence (AI) systems have been developed for early detection of DR and have been included in the American Diabetes Association's guidelines for screening in adults. We sought to determine the diagnostic efficacy of autonomous AI for the diabetic eye exam in youth with diabetes. RESEARCH DESIGN AND METHODSIn this prospective study, point-of-care diabetic eye exam was implemented using a nonmydriatic fundus camera with an autonomous AI system for detection of DR in a multidisciplinary pediatric diabetes center. Sensitivity, specificity, and diagnosability of AI was compared with consensus grading by retinal specialists, who were masked to AI output. Adherence to screening guidelines was measured before and after AI implementation. RESULTSThree hundred ten youth with diabetes aged 5-21 years were included, of whom 4.2% had DR. Diagnosability of AI was 97.5% (302 of 310). The sensitivity and specificity of AI to detect more-than-mild DR was 85.7% (95% CI 42.1-99.6%) and 79.3% (74.3-83.8%), respectively, compared with the reference standard as defined by retina specialists. Adherence improved from 49% to 95% after AI implementation. CONCLUSIONSUse of a nonmydriatic fundus camera with autonomous AI was safe and effective for the diabetic eye exam in youth in our study. Adherence to screening guidelines improved with AI implementation. As the prevalence of diabetes increases in youth and adherence to screening guidelines remains suboptimal, effective strategies for diabetic eye exams in this population are needed.Diabetes is a significant public health problem worldwide, and in the U.S., it affects almost 30 million people (1). In youth, diabetes is one of the most common chronic childhood diseases, with an incidence that has been increasing over the past decade for both type 1 diabetes (T1D) and type 2 diabetes (T2D) (2). Children with diabetes are at risk for diabetes-related complications, including diabetic retinopathy (DR), which

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Diabetic Retinopathy Screening Ratio Is Improved When Using a Digital, Nonmydriatic Fundus Camera Onsite in a Diabetes Outpatient Clinic

Cited by 14 publications

References 45 publications

Diagnostic Accuracy of Detecting Diabetic Retinopathy by Using Digital Fundus Photographs in the Peripheral Health Facilities of Bangladesh: Validation Study

Diagnostic Accuracy of Detecting Diabetic Retinopathy by Using Digital Fundus Photographs in the Peripheral Health Facilities of Bangladesh: Validation Study

Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes

The SEE Study: Safety, Efficacy, and Equity of Implementing Autonomous Artificial Intelligence for Diagnosing Diabetic Retinopathy in Youth

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