https://www.selleckchem.com/products/ro-3306.html To evaluate the performance of an AI-based diabetic retinopathy (DR) grading model in real-world community clinical setting. Participants with diabetes on record in the chosen community were recruited by health care staffs in a primary clinic of Zhengzhou city, China. Retinal images were prospectively collected during December 2018 and April 2019 based on intent-to-screen principle. A pre-validated AI system based on deep learning algorithm was deployed to screen DR graded according to the International Clinical Diabetic Retinopathy scale. Kappa value of DR severity, the sensitivity, specificity of detecting referable DR (RDR) and any DR were generated based on the standard of the majority manual grading decision of a retina specialist panel. Of the 193 eligible participants, 173 (89.6%) were readable with at least one eye image. Mean [SD] age was 69.3 (9.0) years old. Total of 321 eyes (83.2%) were graded both by AI and the specialist panel. The κ value in eye image grading was 0.715. The sensitivity, onsistency was found between AI and manual grading. These prospective evidences were essential for regulatory approval. To compare two commercially available staining solutions (MembraneBlue Dual® by D.O.R.C., Netherlands, and TWIN by AL.CHI.MI.A. S.R.L., Italy), in terms of intraoperative handling, staining efficacy and safety, in eyes undergoing surgery for idiopathic epiretinal membrane (ERM). In this observational cross-sectional study, the performance of the dyes used during the procedure (cohesion, ERM and internal limiting membrane [ILM] staining efficacy) was scored by the surgeon using a customized questionnaire after 10 procedures with each of the two dyes. Best-corrected visual acuity (BCVA), central foveal thickness (CFT), blue-light fundus autofluorescence (BAF), and microperimetry-determined retinal sensitivity were reviewed preoperatively and then at 1 and 3months after surgery. ILM staining efficacy wit