METHODS: This 5-year, prospective, multicenter, observational, study enrolled 30,138 patients across all approved ranibizumab indications from outpatient ophthalmology clinics. 297 consenting patients (≥18 years) with mCNV who were treatment-naïve or prior-treated with ranibizumab or other ocular treatments were enrolled, and treated with ranibizumab according to the local product label. The main outcomes are visual acuity (VA; Early Treatment Diabetic Retinopathy Study letters or equivalent), adverse events during the study, and treatment exposure over 1 year. Results are presented by prior treatment status of the study eye and injection frequency.

RESULTS: Of the 297 mCNV patients recruited in the study, 108 were treatment-naïve and 175 were prior ranibizumab-treated. At baseline, the mean age of patients was 57.6 years, and 59.0 years and 80.6% and 65.7% were female in the treatment-naïve and prior ranibizumab-treated groups, respectively. Most were Caucasian (treatment-naïve, 88.9%; prior ranibizumab-treated, 86.9%). The mean (±standard deviation [SD]) VA letter changes to 1 year were +9.7 (±17.99) from 49.5 (±20.51) and +1.5 (±13.15) from 58.5 (±19.79) and these were achieved with a mean (SD) of 3.0 (±1.58) and 2.6 (±2.33) injections in the treatment-naïve and prior ranibizumab-treated groups, respectively. Presented by injection frequencies 1-2, 3-4 and ≥5 injections in Year 1, the mean (SD) VA changes were +15.0 (±14.70), +7.7 (±19.91) and -0.7 (±16.05) in treatment-naïve patients and +1.5 (±14.57), +3.1 (±11.53) and -3.6 (±11.97) in prior ranibizumab-treated patients, respectively. The safety profile was comparable with previous ranibizumab studies.

METHODS: This was a 24-month, phase 4, open-label, single-arm, prospective, observational study conducted at 20 specialised retinal centres in Japan. Participants were 209 patients with DME and impaired VA, not previously treated with either intravitreal or systemic anti-vascular endothelial growth factor (anti-VEGF) agents, who initiated ranibizumab 0.5 mg per investigator discretion. Following ranibizumab administration, patients were treated per routine clinical practice. Other treatments were allowed. The main outcome measure was the mean change in best-corrected VA (BCVA) in logarithmic minimum angle of resolution (logMAR) from baseline to month 12. An exploratory objective was to assess patients' psychological status using the Hospital Anxiety and Depression Scale (HADS).

RESULTS: The mean ± standard deviation BCVA at baseline was 0.43 ± 0.39 logMAR. The mean number of injections of ranibizumab and anti-VEGF agents from baseline to month 11 was 3.2 ± 2.0 and 3.6 ± 2.4, respectively. The BCVA change from baseline to 12 months was - 0.08 ± 0.34 logMAR (p = 0.011), showing a significant improvement; the HADS-anxiety score also decreased significantly (p = 0.001) and the depression score decreased numerically (p = 0.080).

METHODS: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.

RESULTS: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83.