We demonstrated that OCTA can identify preclinical DR before the manifestation of clinically apparent retinopathy in diabetic eyes. DM2 patients without DR have SCP, DCP and choriocapillaris impairment. Our results suggested that OCTA might be a promising tool for regular screening of diabetic eyes for DR.

BACKGROUND AND OBJECTIVE: To compare the macular perfusion in the retina and choroidal layer between control subjects and Chinese patients with diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA) and to evaluate the association of OCTA characteristics with the stage of DR. PATIENTS AND METHODS: A total of 200 eyes (normal controls = 40; mild non-proliferative diabetic retinopathy [NPDR] = 40; moderate NPDR = 40; severe NPDR = 40; and PDR [proliferative diabetic retinopathy] = 40) underwent OCTA imaging. OCTA parameters were vessel densities in the superficial capillary plexus (SCP), deep capillary plexus (DCP), and choriocapillaris, as well as foveal avascular zone (FAZ) area (mm 2 ) in the SCP. RESULTS: The reduction of macular perfusion in the SCP, DCP, and choriocapillaris was correlated with increasing severity of DR. Vessel density in the SCP, DCP, and choriocapillaris was 55.31% ± 2.56%, 62.40% ± 2.46%, and 66.87% ± 1.30%, respectively, in control subjects; 50.58% ± 3.14%, 56.31% ± 4.24%, and 66.20% ± 1.69%, respectively, in mild NPDR; 46.46% ± 3.09%, 49.40% ± 5.68%, and 64.39% ± 1.94%, respectively, in moderate NPDR; 45.61% ± 3.81%, 49.33% ± 6.14%, and 63.75% ± 2.21%, respectively, in severe NPDR; and 43.78% ± 3.71%, 44.78% ± 6.36%, and 61.32% ± 6.29%, respectively, in PDR. Vessel density in DR groups decreased compared with normal controls ( P < .001). FAZ area in the SCP was 0.34 ± 0.09 mm 2 in control subjects compared with 0.48 ± 0.17 mm 2 (mild NPDR), 0.52 ± 0.13 mm 2 (moderate NPDR), 0.62 ± 0.24 mm 2 (severe NPDR), and 0.75 ± 0.30 mm 2 (PDR). FAZ in the SCP of patients with DR was greater than that in control subjects ( P < .001). Vessel density in the DCP shows better ability to identify the severity of DR (area under the curve, sensitivity, and specificity of 0.967, 92.5%, and 93.1%, respectively) than vessel density in the SCP and choriocapillaris. CONCLUSION: OCTA might be clinically useful to evaluate different stages of DR in a noninvasive manner. Vessel density in DCP could be an objective and reliable indicator for monitoring progression of DR. [ Ophthalmic Surg Lasers Imaging Retina. 2019;50:e88–e95.]

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Purpose: To development and validation of machine learning-based classifiers based on simple non-ocular metrics for detecting referable diabetic retinopathy (RDR) in a large-scale Chinese population–based survey.Methods: The 1,418 patients with diabetes mellitus from 8,952 rural residents screened in the population-based Dongguan Eye Study were used for model development and validation. Eight algorithms [extreme gradient boosting (XGBoost), random forest, naïve Bayes, k-nearest neighbor (KNN), AdaBoost, Light GBM, artificial neural network (ANN), and logistic regression] were used for modeling to detect RDR in individuals with diabetes. The area under the receiver operating characteristic curve (AUC) and their 95% confidential interval (95% CI) were estimated using five-fold cross-validation as well as an 80:20 ratio of training and validation.Results: The 10 most important features in machine learning models were duration of diabetes, HbA1c, systolic blood pressure, triglyceride, body mass index, serum creatine, age, educational level, duration of hypertension, and income level. Based on these top 10 variables, the XGBoost model achieved the best discriminative performance, with an AUC of 0.816 (95%CI: 0.812, 0.820). The AUCs for logistic regression, AdaBoost, naïve Bayes, and Random forest were 0.766 (95%CI: 0.756, 0.776), 0.754 (95%CI: 0.744, 0.764), 0.753 (95%CI: 0.743, 0.763), and 0.705 (95%CI: 0.697, 0.713), respectively.Conclusions: A machine learning–based classifier that used 10 easily obtained non-ocular variables was able to effectively detect RDR patients. The importance scores of the variables provide insight to prevent the occurrence of RDR. Screening RDR with machine learning provides a useful complementary tool for clinical practice in resource-poor areas with limited ophthalmic infrastructure.

A camera-based model is established to predict the total difference for samples of metallic panels with effect coatings under directional illumination, and the testing results indicate that the model can precisely predict the total difference between samples with metallic coatings with satisfactory consistency to the visual data. Due to the limited amount of testing samples, the model performance should be further developed by increasing the training and testing samples.

Purpose: To compare vessel density in macular and peripapillary area between control subjects and patients with non-proliferative diabetic retinopathy (NPDR) using optical coherence tomography angiography (OCTA) and to evaluate the association between RNFL thickness and different stage of diabetic retinopathy. Methods: A total of 170 eyes (normal control, 43; mild NPDR, 43; moderate NPDR, 42; severe NPDR, 42) underwent OCTA imaging. Optical coherence tomography angiographic parameters were vessel densities in superficial capillary plexus (SCP), deep capillary plexus (DCP) in macular area and peripapillary area. Results: The reduction of vessel density of SCP and DCP in macular area, peripapillary area as well as RNFL thickness were correlated with increasing severity of DR. Vessel density of SCP and DCP in macular area, peripapillary area and FD300 in NPDR groups decreased as compared to normal control (P<0.001). Vessel density of DCP shows better ability to identify the severity of DR (sensitivity, and specificity of 88.1%, and 85.2%, respectively) than in FD 300, vessel density of SCP in macular area and peripapillary area Conclusion: Macular and peripapillary vessel density as well as RNFL thickness were significantly decreased in different stage of NPDR compared to normal controls. Vessel density in DCP could be an objective and sensitive indicator for monitoring progression of DR. OCTA might be clinically useful to evaluate microvascular and microstructural alterations in macula and ONH, thus providing a new method to study the course of DR. Key Words: Diabetic retinopathy, optical coherence tomography angiography, vessel density, RNFL thickness, FD 300

Effect coatings have the unique property of large change of appearance under different viewing conditions. This results in quality control problems of related products. In this letter, samples of metallic panels with effect coatings are visually assessed and measured. Based on experimental results, we propose formulae to predict precisely the total differences of effective samples in terms of variations in color, coarseness, and glint. Under diffused illumination, the total difference formula includes color difference and coarseness difference. Under directional illumination, the total difference formula includes color difference and glint difference.

Purpose: To compare vessel density in macular and peripapillary area between control subjects and patients with non-proliferative diabetic retinopathy (NPDR) using optical coherence tomography angiography (OCTA) and to evaluate the association between RNFL thickness and different stage of diabetic retinopathy. Methods: A total of 170 eyes (normal control, 43; mild NPDR, 43; moderate NPDR, 42; severe NPDR, 42) underwent OCTA imaging. Optical coherence tomography angiographic parameters were vessel densities of superficial capillary plexus (SCP), deep capillary plexus (DCP) in macular area and peripapillary area. Results: Vessel density of SCP and DCP in macular area, peripapillary area as well as RNFL thickness were 53.13%±3.03%, 52.07%±2.28%, 53.78%±3.66% and 128.86μm±11.32μm, respectively, in control subjects; 50.24%±3.81%, 47.40%±45.02%, 46.50%±6.03% and 124μm±13.97μm, respectively, in mild NPDR; 46.80%±5.23%; 44.39%±3.99%; 44.64%±4.23%; 121.02μm±20.86μm, respectively, in moderate NPDR; 42.82%±5.46%, 42.34%±5.14%, 43.16%±4.47%, 118.60μm±21.91μm in severe NPDR. The reduction of vessel density of SCP and DCP in macular area, peripapillary area as well as RNFL thickness were correlated with increasing severity of DR. Vessel density of SCP and DCP in macular area, peripapillary area and foveal density 300 (FD 300) in normal control were significantly higher than that of mild, moderate and severe NPDR groups. (all P<0.001). Vessel density of DCP shows better ability to identify the severity of DR (0.913; 95% CI=0.867-0.958; cut off value:0.75) than FD 300, vessel density of SCP in macular area and peripapillary area. Conclusion: Macular and peripapillary vessel density as well as RNFL thickness decreased as DR progresses. Vessel density in DCP could be an objective and sensitive indicator for monitoring progression of DR. OCTA might be clinically useful to evaluate microvascular and microstructural alterations in macula and optive nerve head (ONH), thus providing a new method to study the course of DR.