Mobile-based Risk Assessment of Diabetic Retinopathy using a Smartphone and Adapted Ophtalmoscope

Felgueiras, Simão; Costa, Joao Guilherme Cavalcanti; Gonçalves, João; Soares, Filipe

doi:10.5220/0006599701680175

Cited by 3 publications

(6 citation statements)

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“…However, the CI for prevalence includes the prevalence used for the sample size calculation. On the other hand, we assumed a sensitivity of 70% for sample size calculation, having taken into account a previous study [14] with an old version of the algorithm and conducted with patients with DR screenpositives only, which may have led to a higher precision.…”

Section: Discussionmentioning

confidence: 99%

“…Sample size calculation was made for a binary test outcome and the following aspects: an expected sensitivity of at least 70% [14] (given the result of a preliminary study with the neural network and also because this value is considered to be the lowest value accepted for screening tools [19]), a confidence level of 95%, a type I error of 5%, a power of 95%, a type II error of 12%, and a prevalence of DR of 20% [20]. Accordingly, the required sample size was composed of 286 images.…”

Section: Study Design and Image Selectionmentioning

confidence: 99%

“…The Convolutional Neural Network (CNN) model with Inception-V3 is an artificial neural network based on deep learning for automated detection of DR [13]. In a previous pilot study to determine preliminary safety and performance with individuals with diabetes referred to ophthalmology because of DR, the software achieved a sensitivity of 74% and a specificity of 95% [14]. Nevertheless, this innovative diagnostic test needs to be clinically validated in people with the target disease in similar conditions to those in which the test is intended to be used [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment

et al. 2020

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Purpose: To evaluate the diagnostic accuracy of a diagnostic system software for the automated screening of diabetic retinopathy (DR) on digital colour fundus photographs, the 2019 Convolutional Neural Network (CNN) model with Inception-V3. Methods: In this cross-sectional study 295 fundus images were analysed by the CNN model and compared to a panel of ophthalmologists. Images were obtained from a dataset acquired within a screening programme. Diagnostic accuracy measures and respective 95% confidence intervals (CI) were calculated. Results: The sensitivity and specificity of the CNN model in diagnosing referable DR was 81% [95% confidence interval (CI), 66%−90%] and 97% (95% CI, 95%−99%), respectively. Positive predictive value was 86% (95% CI, 72%−94%) and negative predictive value 96% (95% CI, 93%−98%). The positive likelihood ratio was 33 (95% CI, 15−75) and the negative was 0.20 (95% CI, 0.11−0.35). Its clinical impact is demonstrated by the change observed in the pre-test probability of referable DR (assuming a prevalence of 16%) to a post-test probability for a positive test result of 86% and for a negative test result of 4%. Conclusion: A CNN model negative test result safely excludes DR and its use may significantly reduce the burden of ophthalmologists at reading centres.

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Section: Discussionmentioning

confidence: 99%

Section: Study Design and Image Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment

et al. 2020

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“…The computer-aided diagnosis (CADx) system for diabetic retinopathy screening based on deep learning integrated with EyeFundusScope (Fraunhofer AICOS, Porto, Portugal) smartphone-based retinal camera was assessed on images acquired with EyeFundusScope [43] in a pilot study (in this study a commercial ophthalmoscope was connected to a smartphone) and showed high sensitivity and specificity (67% and 95% respectively) without pupil dilation drops [44]. The study was conducted with patients with known diabetic retinopathy at an ophthalmology outpatient clinic, using indirect ophthalmoscopy as the comparator.…”

Section: Introduction 1background and Rationalementioning

confidence: 99%

“…Therefore, a comparison with the reference standard for screening (tabletop fundus camera) in a generalizable sample of individuals with diabetes, to assess the ability to discriminate patients with diabetic retinopathy from those without it [46] is lacking. Moreover, in that study, EyeFundusScope was operated by a highly trained technical professional [44]. However, since image acquisition with smartphone-based fundus cameras can be challenging for beginners, we need to assess the quality of images and the diagnostic accuracy of EyeFundusScope when operated by their potential future users-nurses and physicians without training in ophthalmology.…”

Section: Introduction 1background and Rationalementioning

confidence: 99%

Implementation and Evaluation of a Mobile Retinal Image Acquisition System for Screening Diabetic Retinopathy: Study Protocol

et al. 2022

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Screening diabetic retinopathy, a major cause of blindness, is time-consuming for ophthalmologists and has some constrains in achieving full coverage and attendance. The handheld fundus camera EyeFundusScope was recently developed to expand the scale of screening, drawing on images acquired in primary care and telescreening made by ophthalmologists or a computer-aided diagnosis (CADx) system. This study aims to assess the diagnostic accuracy of the interpretation of images captured using EyeFundusScope and perform its technical evaluation, including image quality, functionality, usability, and acceptance in a real-world clinical setting. Physicians and nurses without training in ophthalmology will use EyeFundusScope to take pictures of the retinas of patients with diabetes and the images will be classified for the presence or absence of diabetic retinopathy and image quality by a panel of ophthalmologists. A subgroup of patients will also be examined with the reference standard tabletop fundus camera. Screening results provided by the CADx system on images taken with EyeFundusScope will be compared against the ophthalmologists’ analysis of images taken with the tabletop fundus camera. Diagnostic accuracy measures with 95% confidence intervals (CIs) will be calculated for positive and negative test results. Proportion of each category of image quality will be presented. Usability and acceptance results will be presented qualitatively.

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Smartphone Imaging Devices for Screening of Diabetic Retinopathy: A Review

Odigie MO,

Akinlabi GA

2022

Ibom Med J

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Background: Innovative solutions are needed for retinal fundus examination in diabetic individuals as a result of the projected rise in the global prevalence of Diabetic Retinopathy (DR) in the near future. Early detection of DR will help to prevent avoidable blindness and lead to improvement of eyecare services for diabetic individuals. Currently, smartphone fundoscopy is emerging as new form of retinal examination because of the several advantages it offers, such as lower cost and portability, compared to the conventional desktop cameras. This review seeks to describe the characteristics of commercially available smartphone imaging devices.Methodology: The Google search engine was used to investigate the details of different smartphone imaging devices currently available for fundus examination. Studies that have evaluated these devices for the detection of DR were also reported. Results: Findings from this investigation revealed that some of these devices have the potential to detect features of DR which could be beneficial to diabetic individuals living in developing nations. Conclusion: Further research comparing various smartphone imaging devices for DR detection should be carried out by health care professionals across various populations.

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Mobile-based Risk Assessment of Diabetic Retinopathy using a Smartphone and Adapted Ophtalmoscope

Cited by 3 publications

References 0 publications

Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment

Screening for Diabetic Retinopathy Using an Automated Diagnostic System Based on Deep Learning: Diagnostic Accuracy Assessment

Implementation and Evaluation of a Mobile Retinal Image Acquisition System for Screening Diabetic Retinopathy: Study Protocol

Smartphone Imaging Devices for Screening of Diabetic Retinopathy: A Review

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