Objectives.The foveal avascular zone (FAZ) is a biomarker for quantifying diabetic macular ischemia (DMI), to automate the identification and quantification of the FAZ in DMI, using an improved U-Net convolutional neural network (CNN) and to establish a CNN model based on optical coherence tomography angiography (OCTA) images for the same purpose. Methods. The FAZ boundaries on the full-thickness retina of en face OCTA images of DMI and normal eyes were manually marked. Seventy percent of OCTA images were used as the training set, and ten percent of these images were used as the validation set to train the improved U-Net CNN with two attention modules. Finally, twenty percent of the OCTA images were used as the test set to evaluate the accuracy of this model relative to that of the baseline U-Net model. This model was then applied to the public data set sFAZ to compare its effectiveness with existing models at identifying and quantifying the FAZ area. Results. This study included 110 OCTA images. The Dice score of the FAZ area predicted by the proposed method was 0.949, the Jaccard index was 0.912, and the area correlation coefficient was 0.996. The corresponding values for the baseline U-Net were 0.940, 0.898, and 0.995, respectively, and those based on the description data set sFAZ were 0.983, 0.968, and 0.950, respectively, which were better than those previously reported based on this data set. Conclusions. The improved U-Net CNN was more accurate at automatically measuring the FAZ area on the OCTA images than the traditional CNN. The present model may measure the DMI index more accurately, thereby assisting in the diagnosis and prognosis of retinal vascular diseases such as diabetic retinopathy.

Full-thickness macular hole (FTMH) leads to central vision loss. It is essential to identify patients with FTMH at high risk of postoperative failure accurately to achieve anatomical closure. This study aimed to construct a predictive model for the anatomical outcome of FTMH after surgery. A retrospective study was performed, analyzing 200 eyes from 197 patients diagnosed with FTMH. Radiomics features were extracted from optical coherence tomography (OCT) images. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained and evaluated. Decision curve analysis and survival analysis were performed to assess the clinical implications. Sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC) were calculated to assess the model effectiveness. In the training set, the AUC values were 0.998, 0.988, and 0.995, respectively. In the test set, the AUC values were 0.941, 0.943, and 0.968, respectively. The OCT-omics scores were significantly higher in the “Open” group than in the “Closed” group and were positively correlated with the minimum diameter (MIN) and base diameter (BASE) of FTMH. Therefore, in this study, we developed a model with robust discriminative ability to predict the postoperative anatomical outcome of FTMH.

This study compared the absorption of subretinal fluid (SRF) in patients with rhegmatogenous retinal detachment (RRD) with and without high myopia after scleral buckling (SB) and investigated the effect of high myopia on SRF absorption. This retrospective study included patients with primary macula-off RRD grouped according to myopia and age. The optical coherence tomography (OCT) and OCT angiography indicators included subretinal fluid height (SRFH), subfoveal choroidal thickness (SFCT), and choroidal capillary blood flow density (CCFD) measured regularly. The presence of SRF 3 months after surgery was defined as delayed absorption. Overall, 90 eyes of 89 patients were enrolled, and 46 eyes (51.11%) had high myopia. In 43 eyes (47.78%), SRF absorption was delayed. There was no significant difference in SRF absorption after SB between the high and non-high myopia groups; younger patients (<35 years) had a higher probability of delayed absorption (p < 0.05). The SFCT in high myopia was significantly thinner than that in the non-high myopia group (p < 0.05); SFCT and SRFH were positively correlated (r_s = 0.275, p = 0.002), and there was a significant difference between the average CCFD with and without SRF (p < 0.05). High myopia had no significant effect on SRF absorption after SB.