Supplemental Digital Content is Available in the Text.Controversies about characteristics of Eales disease and the efficacy of different therapies have lasted for decades. Here, all available studies were analyzed and clarified the epidemiology, etiology, symptoms, clinical manifestations, and complications of Eales disease. In addition, we also made a comprehensive summary of the current treatments for neovascular complications.

Purpose To compare the three-dimensional (3D) heads-up surgery with the traditional microscopic (TM) surgery for various vitreoretinal diseases. Methods A medical record review of patients that underwent 3D heads-up or TM vitreoretinal surgeries was performed from May 2020 to October 2021 in this retrospective case–control study. Main outcome measures included surgery-related characteristics, efficacy, safety, and satisfaction feedback from the surgical team. Results A total of 220 (47.6%) and 242 (52.4%) eyes were included in the 3D and TM groups, respectively. The 3D heads-up system significantly benefits delicate surgical steps, like the epiretinal membrane (ERM) peeling for ERM and internal limiting membrane peeling for idiopathic macular holes (P < 0.05). The 3D heads-up system could facilitate a significantly better visual outcome for pathologic myopic foveoschisis (P = 0.049), while no difference by TM surgery (P = 0.45). For the satisfaction feedback, the 3D heads-up system was rated significantly higher in most subscales and the overall score (P < 0.05). The surgeons’ ratings on operating accuracy and the first assistants’ rating on operating accuracy and operation cooperation were significantly higher in the TM group than in the 3D group (P < 0.05). Besides that, the 3D heads-up surgery was comparable with TM surgery in the surgery-related characteristics, choice of tamponades, postoperative VA, primary anatomic success, and perioperative complications (P > 0.05). Conclusion The efficacy and safety of the 3D heads-up surgery were generally comparable to the TM surgery. The 3D heads-up system could significantly benefit delicate surgical steps and achieve better surgical team satisfaction.

PurposeTo evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) hierarchical diagnosis software, EyeWisdom V1 for diabetic retinopathy (DR).Materials and MethodsThe prospective study was a multicenter, double-blind, and self-controlled clinical trial. Non-dilated posterior pole fundus images were evaluated by ophthalmologists and EyeWisdom V1, respectively. The diagnosis of manual grading was considered as the gold standard. Primary evaluation index (sensitivity and specificity) and secondary evaluation index like positive predictive values (PPV), negative predictive values (NPV), etc., were calculated to evaluate the performance of EyeWisdom V1.ResultsA total of 1,089 fundus images from 630 patients were included, with a mean age of (56.52 ± 11.13) years. For any DR, the sensitivity, specificity, PPV, and NPV were 98.23% (95% CI 96.93–99.08%), 74.45% (95% CI 69.95-78.60%), 86.38% (95% CI 83.76-88.72%), and 96.23% (95% CI 93.50-98.04%), respectively; For sight-threatening DR (STDR, severe non-proliferative DR or worse), the above indicators were 80.47% (95% CI 75.07-85.14%), 97.96% (95% CI 96.75-98.81%), 92.38% (95% CI 88.07-95.50%), and 94.23% (95% CI 92.46-95.68%); For referral DR (moderate non-proliferative DR or worse), the sensitivity and specificity were 92.96% (95% CI 90.66-94.84%) and 93.32% (95% CI 90.65-95.42%), with the PPV of 94.93% (95% CI 92.89-96.53%) and the NPV of 90.78% (95% CI 87.81-93.22%). The kappa score of EyeWisdom V1 was 0.860 (0.827-0.890) with the AUC of 0.958 for referral DR.ConclusionThe EyeWisdom V1 could provide reliable DR grading and referral recommendation based on the fundus images of diabetics.

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