IMPORTANCE: There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. BACKGROUND: To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. DESIGN: Retrospective audit. PARTICIPANTS: Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. METHODS: Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve, sensitivity and specificity. RESULTS: For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. CONCLUSIONS AND RELEVANCE: This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema.
IMPORTANCE: There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. BACKGROUND: To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. DESIGN: Retrospective audit. PARTICIPANTS: Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. METHODS: Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve, sensitivity and specificity. RESULTS: For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. CONCLUSIONS AND RELEVANCE: This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema.
Authors: Jakob K H Andersen; Martin S Hubel; Malin L Rasmussen; Jakob Grauslund; Thiusius R Savarimuthu Journal: Transl Vis Sci Technol Date: 2022-06-01 Impact factor: 3.048
Authors: Tyson N Kim; Michael T Aaberg; Patrick Li; Jose R Davila; Malavika Bhaskaranand; Sandeep Bhat; Chaithanya Ramachandra; Kaushal Solanki; Frankie Myers; Clay Reber; Rohan Jalalizadeh; Todd P Margolis; Daniel Fletcher; Yannis M Paulus Journal: Eye (Lond) Date: 2020-04-27 Impact factor: 3.775
Authors: Yi-Zhong Wang; Daniel Galles; Martin Klein; Kirsten G Locke; David G Birch Journal: Transl Vis Sci Technol Date: 2020-03-17 Impact factor: 3.283