Endoscopic Vitrectomy Combined with 3D Heads-Up Viewing System in Treating Traumatic Ocular Injury.

Purpose: To investigate effects and complications of endoscopic vitrectomy combined with 3D heads-up viewing system in treating traumatic ocular injury. Patients and Methods. This is a retrospective interventional case series in a tertiary referral center in Taiwan, and we included patients of traumatic ocular injury, and they underwent endoscopic vitrectomy combined with a 3D heads-up viewing system. Results: Fourteen eyes of traumatic globe injury from 14 patients were studied over a 30-month period. Preoperative VA ranged from no light perception (NLP) to 6/6. Postoperative visual acuity improved in 11 of the 14 eyes (79%). Until 6 months after surgery, all eyes had attached retina. The median logMAR BCVA was 2.4 at the first visit and 1.19 at the last visit (p = 0.0028). No subject suffered from retinal detachment, endophthalmitis, or other severe complications. Conclusions: Vitrectomy using endoscopy combined with 3D heads-up viewing system allowed early evaluation and intervention in traumatic ocular injuries. Most of our cases showed both anatomical and visual acuity improvements.

Automatic Segment and Quantify Choroid Layer in Myopic eyes: Deep Learning based Model.

PURPOSE: To report a rapid and accurate method based upon deep learning for automatic segmentation and measurement of the choroidal thickness (CT) in myopic eyes, and to determine the relationship between refractive error (RE) and CT. METHODS: Fifty-four healthy subjects 20-39 years of age were retrospectively reviewed. Data reviewed included age, gender, laterality, visual acuity, RE, and Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT) images. The choroid layer was labeled by manual and automatic method using EDI-OCT. A Mask Region-convolutional Neural Network (Mask R-CNN) model, using deep Residual Network (ResNet) and Feature Pyramid Networks (FPN) as a backbone network, was trained to automatically outline and quantify the choroid layer. RESULTS: ResNet 50 model was adopted for its 90% accuracy rate and 6.97 s average execution time. CT determined by the manual method had a mean thickness of 258.75 ± 66.11 µm, a positive correlation with RE (r = 0.596, p < .01) and significant association with gender (p = .011) and RE (p < .001) in multivariable linear regression analysis. Meanwhile, CT determined by deep learning presented a mean thickness of 226.39 ± 54.65 µm, a positive correlation with RE (r = 0.546, p < .01) and significant association with gender (p = .043) and RE (p < .001) in multivariable linear regression analysis. Both methods revealed that CT decreased with the increase in myopic RE. CONCLUSIONS: This deep learning method using Mask-RCNN was able to successfully determine the relationship between RE and CT in an accurate and rapid way. It could eliminate the need for manual process, while demonstrating a feasible clinical application.

The Clinical Influence after Implementation of Convolutional Neural Network-Based Software for Diabetic Retinopathy Detection in the Primary Care Setting.

Deep learning-based software is developed to assist physicians in terms of diagnosis; however, its clinical application is still under investigation. We integrated deep-learning-based software for diabetic retinopathy (DR) grading into the clinical workflow of an endocrinology department where endocrinologists grade for retinal images and evaluated the influence of its implementation. A total of 1432 images from 716 patients and 1400 images from 700 patients were collected before and after implementation, respectively. Using the grading by ophthalmologists as the reference standard, the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to detect referable DR (RDR) were 0.91 (0.87-0.96), 0.90 (0.87-0.92), and 0.90 (0.87-0.93) at the image level; and 0.91 (0.81-0.97), 0.84 (0.80-0.87), and 0.87 (0.83-0.91) at the patient level. The monthly RDR rate dropped from 55.1% to 43.0% after implementation. The monthly percentage of finishing grading within the allotted time increased from 66.8% to 77.6%. There was a wide range of agreement values between the software and endocrinologists after implementation (kappa values of 0.17-0.65). In conclusion, we observed the clinical influence of deep-learning-based software on graders without the retinal subspecialty. However, the validation using images from local datasets is recommended before clinical implementation.

Combined Endoscope assisted Procedures (CEaP) as a complete treatment for neovascular glaucoma.

PURPOSE: To investigate the effect and complications of Combined Endoscope assisted Procedures (CEaP): endoscopic cyclophotocoagulation and pars plana ablation (ECP-plus), along with endoscopic panretinal photocoagulation (PRP). PATIENTS AND METHODS: The study design is a retrospective and noncomparative interventional case series from a tertiary referral center in Taiwan. Patients experiencing vessel growth at the iris and anterior chamber angle, along with an IOP > 21 mmHg were included. RESULTS: Twenty-five eyes from 23 patients were included over a 24-month period. After the procedures, all of them had a lower IOP value than their preoperative value. The mean IOP was 38.2± 7.1 mm Hg preoperatively, and 10.2± 4.7 mmHg (1 day), 13.8± 4.6 mmHg (1 week), 15.0± 5.3 mmHg (2 weeks), 17.4± 4.7 mmHg (1 month), 16.6± 4.1 mmHg (3 months), 16.0± 5.0 mmHg (6 months), and 15.7± 5.5 mmHg (12 months) postoperatively. At the 6th and 12th months, the IOP stabilized rate was 84% and 75%, respectively. Complications in the initial postoperative period (< 3 months) included uveitis (24%), and hyphema (16%), which were both resolved in the early postoperative period. Complications beyond 6 months included hypotony and phthisis bulbi in two patients (8%) in our study. There was no subject who suffered from retinal detachment, endophthalmitis or any other severe complications. CONCLUSIONS: The results of this study show that CEaP offers positive results in IOP lowering and NV regression. Additionally, CEaP is a complete treatment for NVG in controlling IOP and NV growth. The IOP lowering effects can be sustained upon completion of the treatment.