Background: The aim of this study was to compare the therapeutic effect of intravitreal treatment with ranibizumab and dexamethasone using specific swept-source optical coherence tomography retinal biomarkers in patients with diabetic macular edema (DME). Methods: 156 treatment-naïve patients with DME were divided in two groups: 75 patients received 3 monthly intravitreal injections of ranibizumab 0.5 mg (Lucentis®) (Group 1) and 81 patients received an intravitreal implant of dexamethasone 0.7 mg (Ozurdex®) (Group 2). Patients were evaluated at baseline (V1), at three months post-treatment in Group 1, and at two months post-treatment in Group 2 (V2). Best-corrected visual acuity (BCVA) and swept source-OCT were recorded at each interval. Changes between V1 and V2 were analyzed using the Wilcoxon test and differences between the two groups of treatment were assessed using the Mann–Whitney test. Multiple regression analysis was performed to evaluate the possible OCT biomarker (CRT, ICR, CT, SND, HRS) as predictive factors for final visual acuity improvement. Results: In both groups, BCVA improved (p-value < 0.0001), and a significant reduction in central retinal thickness, intra-retinal cysts, red dots, hyper-reflective spots (HRS), and serous detachment of neuro-epithelium (SDN) was observed. A superiority of dexamethasone over ranibizumab in reducing the SDN height (p-value = 0.03) and HRS (p-value = 0.01) was documented. Conclusions: Ranibizumab and dexamethasone are effective in the treatment of DME, as demonstrated by functional improvement and morphological biomarker change. DME associated with SDN and HRS represents a specific inflammatory pattern for which dexamethasone appears to be more effective.

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Purpose: To assess the diagnostic accuracy measures such as sensitivity and specificity of smartphone-based artificial intelligence (AI) approaches in the detection of diabetic retinopathy (DR). Methods: A literature search of the EMBASE and MEDLINE databases (up to March 2020) was conducted. Only studies using both smartphone-based cameras and AI software for image analysis were included. The main outcome measures were pooled sensitivity and specificity, diagnostic odds ratios and relative risk of smartphone-based AI approaches in detecting DR (of all types), and referable DR (RDR) (moderate nonproliferative retinopathy or worse and/or the presence of diabetic macular edema). Results: Smartphone-based AI has a pooled sensitivity of 89.5% (95% confidence interval [CI]: 82.3%–94.0%) and pooled specificity of 92.4% (95% CI: 86.4%–95.9%) in detecting DR. For referable disease, sensitivity is 97.9% (95% CI: 92.6%-99.4%), and the pooled specificity is 85.9% (95% CI: 76.5%–91.9%). The technology is better at correctly identifying referable retinopathy. Conclusions: The smartphone-based AI programs demonstrate high diagnostic accuracy for the detection of DR and RDR and are potentially viable substitutes for conventional diabetic screening approaches. Further, high-quality randomized controlled trials are required to establish the effectiveness of this approach in different populations.

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