Improved Automated Screening of Diabetic Retinopathy

Oliveira, Carlos Manta; Cristóvão, Luis M; Ribeiro, Maria Luísa; Abreu, J. R. Faria de

doi:10.1159/000330285

Cited by 53 publications

(57 citation statements)

References 35 publications

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“…38 One study showed that Retmarker can detect referable retinopathy with a sensitivity of 95.8% and a specificity of 63.2%. 39 Retmarker can also perform predictive longitudinal analysis using microaneurysm turnover. Two independent prospective longitudinal studies using the Retmarker on single-field images have demonstrated the relationship of increased microaneurysm turnover rates identified by the Retmarker program and an increased rate for developing clinically significant macular oedema.…”

Section: Idx-drmentioning

confidence: 99%

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An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness

Tufail

Kapetanakis

Salas-Vega

et al. 2016

Health Technol Assess

106

128

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Background Diabetic retinopathy screening in England involves labour-intensive manual grading of retinal images. Automated retinal image analysis systems (ARIASs) may offer an alternative to manual grading. Objectives To determine the screening performance and cost-effectiveness of ARIASs to replace level 1 human graders or pre-screen with ARIASs in the NHS diabetic eye screening programme (DESP). To examine technical issues associated with implementation. Design Observational retrospective measurement comparison study with a real-time evaluation of technical issues and a decision-analytic model to evaluate cost-effectiveness. Setting A NHS DESP. Participants Consecutive diabetic patients who attended a routine annual NHS DESP visit. Interventions Retinal images were manually graded and processed by three ARIASs: iGradingM (version 1.1; originally Medalytix Group Ltd, Manchester, UK, but purchased by Digital Healthcare, Cambridge, UK, at the initiation of the study, purchased in turn by EMIS Health, Leeds, UK, after conclusion of the study), Retmarker (version 0.8.2, Retmarker Ltd, Coimbra, Portugal) and EyeArt (Eyenuk Inc., Woodland Hills, CA, USA). The final manual grade was used as the reference standard. Arbitration on a subset of discrepancies between manual grading and the use of an ARIAS by a reading centre masked to all grading was used to create a reference standard manual grade modified by arbitration. Main outcome measures Screening performance (sensitivity, specificity, false-positive rate and likelihood ratios) and diagnostic accuracy [95% confidence intervals (CIs)] of ARIASs. A secondary analysis explored the influence of camera type and patients’ ethnicity, age and sex on screening performance. Economic analysis estimated the cost per appropriate screening outcome identified. Results A total of 20,258 patients with 102,856 images were entered into the study. The sensitivity point estimates of the ARIASs were as follows: EyeArt 94.7% (95% CI 94.2% to 95.2%) for any retinopathy, 93.8% (95% CI 92.9% to 94.6%) for referable retinopathy and 99.6% (95% CI 97.0% to 99.9%) for proliferative retinopathy; and Retmarker 73.0% (95% CI 72.0% to 74.0%) for any retinopathy, 85.0% (95% CI 83.6% to 86.2%) for referable retinopathy and 97.9% (95% CI 94.9 to 99.1%) for proliferative retinopathy. iGradingM classified all images as either ‘disease’ or ‘ungradable’, limiting further iGradingM analysis. The sensitivity and false-positive rates for EyeArt were not affected by ethnicity, sex or camera type but sensitivity declined marginally with increasing patient age. The screening performance of Retmarker appeared to vary with patient’s age, ethnicity and camera type. Both EyeArt and Retmarker were cost saving relative to manual grading either as a replacement for level 1 human grading or used prior to level 1 human grading, although the latter was less cost-effective. A threshold analysis testing the highest ARIAS cost per patient before which ARIASs became more expensive per appropriate outcome than human grading, when used to replace level 1 grader, was Retmarker £3.82 and EyeArt £2.71 per patient. Limitations The non-randomised study design limited the health economic analysis but the same retinal images were processed by all ARIASs in this measurement comparison study. Conclusions Retmarker and EyeArt achieved acceptable sensitivity for referable retinopathy and false-positive rates (compared with human graders as reference standard) and appear to be cost-effective alternatives to a purely manual grading approach. Future work is required to develop technical specifications to optimise deployment and address potential governance issues. Funding The National Institute for Health Research (NIHR) Health Technology Assessment programme, a Fight for Sight Grant (Hirsch grant award) and the Department of Health’s NIHR Biomedical Research Centre for Ophthalmology at Moorfields Eye Hospital and the University College London Institute of Ophthalmology.

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Section: Idx-drmentioning

confidence: 99%

“…EyeArt and Retmarker in this study produced similar sensitivity to that previously reported on different data sets. 39,42 Health economics comparison with previous literature A number of previous studies have examined the cost-effectiveness of diabetic retinopathy screening (e.g. James et al, 62 Javitt et al…”

Section: Sensitivity and Specificitymentioning

confidence: 99%

An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness

Tufail

Kapetanakis

Salas-Vega

et al. 2016

Health Technol Assess

106

128

View full text Add to dashboard Cite

show abstract

“…Moreover, it includes a proprietary coregistration algorithm that allows comparison within the same retinal location between different screening visits for the same eye [20]. …”

Section: Methodsmentioning

confidence: 99%

“…These programs involve a considerable amount of data to be analyzed by trained readers/graders or by reading centers. With the purpose of reducing the human burden in relation to the grading of the screening images, a number of automated systems allowing classification of images as having/not having lesions of DR have been proposed [18,19,20,21,22]. …”

Section: Introductionmentioning

confidence: 99%

Screening for Diabetic Retinopathy in the Central Region of Portugal. Added Value of Automated ‘Disease/No Disease' Grading

Ribeiro

Oliveira²,

Neves

et al. 2014

Ophthalmologica

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Purpose: To describe the procedures of a nonmydriatic diabetic retinopathy (DR) screening program in the Central Region of Portugal and the added value of the introduction of an automated disease/no disease analysis. Methods: The images from the DR screening program are analyzed in a central reading center using first an automated disease/no disease analysis followed by human grading of the disease cases. The grading scale used is as follows: R0 - no retinopathy, RL - nonproliferative DR, M - maculopathy, RP - proliferative DR and NC - not classifiable. Results: Since the introduction of automated analysis in July 2011, a total of 89,626 eyes (45,148 patients) were screened with the following distribution: R0 - 71.5%, RL - 22.7%, M - 2.2%, RP - 0.1% and NC - 3.5%. The implemented automated system showed the potential for human grading burden reduction of 48.42%. Conclusions: Screening for DR using automated analysis allied to a simplified grading scale identifies DR vision-threatening complications well while decreasing human burden.

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“…The system was later extended to detect changes in vasculature width and appearance/disappearance of lesions [26]. A commercial system also exists for automatically detecting temporal retinal changes due to red-dot-like lesions from a pair of fundus images of the same retina [27], [28].…”

Section: Introductionmentioning

confidence: 99%

An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images

Adal

Etten

Martínez

et al. 2018

IEEE Trans. Biomed. Eng.

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Abstract-People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy (DR). Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal fundus image sets is challenging due to intra-and inter-visit variability in illumination and image quality, the required high registration accuracy, and the subtle appearance of retinal lesions compared to other retinal features. This paper presents a robust and flexible approach for automated detection of longitudinal retinal changes due to small red lesions by exploiting normalized fundus images that significantly reduce illumination variations and improve the contrast of small retinal features. To detect spatio-temporal retinal changes, the absolute difference between the extremes of the multiscale blobness responses of fundus images from two time-points is proposed as a simple and effective blobness measure. DR related changes are then identified based on several intensity and shape features by a support vector machine classifier. The proposed approach was evaluated in the context of a regular diabetic retinopathy screening program involving subjects ranging from healthy (no retinal lesion) to moderate (with clinically relevant retinal lesions) DR levels. Evaluation shows that the system is able to detect retinal changes due to small red lesions with a sensitivity of 80% at an average false positive rate of 1 and 2.5 lesions per eye on small and large fields-of-view of the retina, respectively.

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Improved Automated Screening of Diabetic Retinopathy

Cited by 53 publications

References 35 publications

An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness

An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness

Screening for Diabetic Retinopathy in the Central Region of Portugal. Added Value of Automated ‘Disease/No Disease' Grading

An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images

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