No abstract

The purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device’s built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device’s built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland–Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of − 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of − 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of − 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device’s built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

Purpose: To evaluate and compare three different techniques of inverted internal limiting membrane (ILM) flap in the treatment of large idiopathic full-thickness macular hole. Methods: In a comparative interventional case series, 72 eyes from 72 patients with large (> 400 µm) full-thickness macular hole were randomly enrolled into three different groups: group Ahemicircular ILM peel with temporally hinged inverted flap; group Bcircular ILM peel with temporally hinged inverted flap; and group Ccircular ILM peel with superior inverted flap. Best-corrected visual acuity (BCVA), anatomical closure rate, and ellipsoid zone (EZ) or external limiting membrane (ELM) defects were evaluated preoperatively, at week 1, and months 1, 3 and 6 after surgery. Results: There were 24 eyes in group A, 23 in group B, and 25 in group C. In all three groups, larger diameter macular hole was associated with worse preoperative visual acuity (r=0.625, P<0.001). Mean BCVA improved significantly in all three groups 6 months after surgery (0.91vs 0.55, p<0.001). 6 months after surgery, mean BCVA improved from 0.91 logMAR to 0.52±0.06 in group A, 0.90 to 0.53±0.06 in group B, and 0.91 to 0.55±0.11 in group C. In group A vs. B vs. C, improvement of BCVA was 0.380±0.04 vs. 0.383±0.04 vs. 0.368±0.11 logMAR, with no statistically significant difference between groups (P=0.660). The rate of successful hole closure was 87.5% vs. 91.3% vs. 100%. Although the closure rate was 100% in Group C (circular ILM peel with superiorly hinged inverted flap), this difference was not statistically significant (P=0.115). Conclusion: ILM peel with an inverted flap is a highly effective procedure for the treatment of large, full-thickness macular hole. Different flap techniques have comparable results, indicating that the technique can be chosen based on surgeon preference.