PURPOSE. A growing body of evidence points to complement dysregulation in diabetes. Early studies have indicated the presence of complement components inside the eye in patients with diabetic retinopathy, but these data have been confounded by leakage of proteins from the systemic circulation into the vitreous cavity. METHODS. We took samples of plasma and vitreous from patients with and without proliferative diabetic retinopathy (PDR) and measured levels of 16 complement components as well as albumin. We employed a normalized ratio using local and systemic complement and albumin levels to control for vascular leakage into the vitreous cavity. RESULTS. Before normalizing, we found significantly higher levels of 16 complement components we measured in PDR eyes compared to controls. After normalizing, levels of C4, factor B, and C5 were decreased compared to controls, while C3a and Ba levels were elevated compared to controls. We also found higher ratios of C3a/C3, C5a/C5, and Ba/factor B in PDR eyes compared to controls. CONCLUSIONS. We found evidence of local, intraocular activation of C3, C5, and factor B. The normalized data suggest involvement of the alternative complement pathway. By showing activation of specific complement components in PDR, this study identifies targets for diagnostic and therapeutic potential.

Background The automated screening of patients at risk of developing diabetic retinopathy represents an opportunity to improve their midterm outcome and lower the public expenditure associated with direct and indirect costs of common sight-threatening complications of diabetes. Objective This study aimed to develop and evaluate the performance of an automated deep learning–based system to classify retinal fundus images as referable and nonreferable diabetic retinopathy cases, from international and Mexican patients. In particular, we aimed to evaluate the performance of the automated retina image analysis (ARIA) system under an independent scheme (ie, only ARIA screening) and 2 assistive schemes (ie, hybrid ARIA plus ophthalmologist screening), using a web-based platform for remote image analysis to determine and compare the sensibility and specificity of the 3 schemes. Methods A randomized controlled experiment was performed where 17 ophthalmologists were asked to classify a series of retinal fundus images under 3 different conditions. The conditions were to (1) screen the fundus image by themselves (solo); (2) screen the fundus image after exposure to the retina image classification of the ARIA system (ARIA answer); and (3) screen the fundus image after exposure to the classification of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists’ classification in each condition and the result from the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of 3 retina specialists for each fundus image. Results The ARIA system was able to classify referable vs nonreferable cases with an area under the receiver operating characteristic curve of 98%, a sensitivity of 95.1%, and a specificity of 91.5% for international patient cases. There was an area under the receiver operating characteristic curve of 98.3%, a sensitivity of 95.2%, and a specificity of 90% for Mexican patient cases. The ARIA system performance was more successful than the average performance of the 17 ophthalmologists enrolled in the study. Additionally, the results suggest that the ARIA system can be useful as an assistive tool, as sensitivity was significantly higher in the experimental condition where ophthalmologists were exposed to the ARIA system’s answer prior to their own classification (93.3%), compared with the sensitivity of the condition where participants assessed the images independently (87.3%; P =.05). Conclusions These results demonstrate that both independent and assistive use cases of the ARIA system present, for Latin American countries such as Mexico, a substantial opportunity toward expanding the monitoring capacity for the early detection of diabetes-related blindness.

Purpose A growing body of evidence suggests complement dysregulation is present in the vitreous of patients with diabetic eye disease. Further translational study could be simplified if aqueous—as opposed to vitreous—were used to sample the intraocular complement environment. Here, we analyze aqueous samples and assess whether a correlation exists between aqueous and vitreous complement levels. Methods We collected aqueous, vitreous, and plasma samples from patients with and without proliferative diabetic retinopathy (PDR) undergoing vitrectomy. We assessed correlation between complement levels in aqueous and vitreous samples after using a normalizing ratio to correct for vascular leakage. Spearman correlation coefficients were used to assess the correlation between complement levels in the aqueous and vitreous. Results Aqueous samples were obtained from 17 cases with PDR and 28 controls. In all patients, aqueous Ba, C3a, and albumin levels were strongly correlated with vitreous levels (Spearman correlation coefficient of 0.8 for Ba and C3a and 0.7 for albumin; all P values < 0.0001). In PDR eyes only, aqueous and vitreous C3a levels were significantly correlated (Spearman correlation coefficient 0.7; P = 0.002), whereas in control eyes, both Ba and C3a (Spearman correlation coefficients of 0.7; P < 0.0001) were significantly correlated. Conclusions A strong correlation exists between aqueous and vitreous complement levels in diabetic eye disease. Translational Relevance The results establish that accurate sampling of the intraocular complement can be done by analyzing aqueous specimens, allowing for the rapid and safe measurement of experimental complement targets and treatment response.

BACKGROUND The automated screening of patients at risk of developing diabetic retinopathy (DR) represents an opportunity to improve their mid-term outcome, and lower the public expenditure associated with direct and indirect costs of common sight-threatening complications of diabetes. OBJECTIVE The present study, aims to develop and evaluate the performance of an automated deep learning–based system to classify retinal fundus images from international and Mexican patients, as referable and non-referable DR cases. In particular, the performance of the automated retina image analysis (ARIA) system is evaluated under an independent scheme (i.e. only ARIA screening) and two assistive schemes (i.e., hybrid ARIA + ophthalmologist screening), using a web-based platform for remote image analysis to determine and compare the sensibility and specificity of the three schemes. METHODS A randomized controlled experiment was performed where seventeen ophthalmologists were asked to classify a series of retinal fundus images under three different conditions: 1) screening the fundus image by themselves (solo), 2) screening the fundus image after being exposed to the retina image classification of the ARIA system (ARIA answer), and 3) screening the fundus image after being exposed to the classification of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists’ classification in each condition and the result from the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of three retina specialists for each fundus image. RESULTS The ARIA system was able to classify referable vs. non-referable cases with an area under the Receiver Operating Characteristic curve (AUROC) of 98.0% and a sensitivity and specificity of 95.1% and 91.5% respectively, for international patient-cases; and an AUROC, sensitivity, and specificity of 98.3%, 95.2%, and 90.0% respectively for Mexican patient-cases. The results achieved outperformed the average performance of the seventeen ophthalmologists enrolled in the study. Additionally, the achieved results suggest that the ARIA system can be useful as an assistive tool, as significant sensitivity improvements were observed in the experimental condition where ophthalmologists were exposed to the ARIA’s system answer previous to their own classification (93.3%), compared to the sensitivity of the condition where participants assessed the images independently (87.3%). CONCLUSIONS These results demonstrate that both use cases of the ARIA system, independent and assistive, present a substantial opportunity for Latin American countries like Mexico, towards an efficient expansion of monitoring capacity for the early detection of diabetes-related blindness.

Background: The automated screening of patients at risk of developing diabetic retinopathy (DR), represents an opportunity to improve their mid-term outcome and lower the public expenditure associated with direct and indirect costs of a common sight-threatening complication of diabetes. Objective: In the present study, we aim at developing and evaluating the performance of an automated deep learning-based system to classify retinal fundus images from international and Mexican patients, as referable and non-referable DR cases. In particular, we study the performance of the automated retina image analysis (ARIA) system under an independent scheme (i.e. only ARIA screening) and two assistive schemes (i.e., hybrid ARIA + ophthalmologist screening), using a web-based platform for remote image analysis. Methods: We ran a randomized controlled experiment where 17 ophthalmologists were asked to classify a series of retinal fundus images under three different conditions: 1) screening the fundus image by themselves (solo), 2) screening the fundus image after being exposed to the opinion of the ARIA system (ARIA answer), and 3) screening the fundus image after being exposed to the opinion of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists' opinion in each condition and the opinion of the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of three retina specialists for each fundus image. Results: The ARIA system was able to classify referable vs. non-referable cases with an area under the Receiver Operating Characteristic curve (AUROC), sensitivity, and specificity of 98%, 95.1% and 91.5% respectively, for international patient-cases; and an AUROC, sensitivity, and specificity of 98.3%, 95.2%, 90% respectively for Mexican patient-cases. The results achieved on Mexican patient-cases outperformed the average performance of the 17 ophthalmologist participants of the study. We also find that the ARIA system can be useful as an assistive tool, as significant specificity improvements were observed in the experimental condition where participants were exposed to the answer of the ARIA system as a second opinion (93.3%), compared to the specificity of the condition where participants assessed the images independently (87.3%). Conclusions: These results demonstrate that both use cases of ARIA systems, independent and assistive, present a substantial opportunity for Latin American countries like Mexico towards an efficient expansion of monitoring capacity for the early detection of diabetes-related blindness.