Aim To explore associations between artificial intelligence (AI)-based fluid compartment quantifications and 12 months visual outcomes in OCT images from a real-world, multicentre, national cohort of naïve neovascular age-related macular degeneration (nAMD) treated eyes. Methods Demographics, visual acuity (VA), drug and number of injections data were collected using a validated web-based tool. Fluid compartment quantifications including intraretinal fluid (IRF), subretinal fluid (SRF) and pigment epithelial detachment (PED) in the fovea (1 mm), parafovea (3 mm) and perifovea (6 mm) were measured in nanoliters (nL) using a validated AI-tool. Results 452 naïve nAMD eyes presented a mean VA gain of +5.5 letters with a median of 7 injections over 12 months. Baseline foveal IRF associated poorer baseline (44.7 vs 63.4 letters) and final VA (52.1 vs 69.1), SRF better final VA (67.1 vs 59.0) and greater VA gains (+7.1 vs +1.9), and PED poorer baseline (48.8 vs 57.3) and final VA (55.1 vs 64.1). Predicted VA gains were greater for foveal SRF (+6.2 vs +0.6), parafoveal SRF (+6.9 vs +1.3), perifoveal SRF (+6.2 vs −0.1) and parafoveal IRF (+7.4 vs +3.6, all p<0.05). Fluid dynamics analysis revealed the greatest relative volume reduction for foveal SRF (−16.4 nL, −86.8%), followed by IRF (−17.2 nL, −84.7%) and PED (−19.1 nL, −28.6%). Subgroup analysis showed greater reductions in eyes with higher number of injections. Conclusion This real-world study describes an AI-based analysis of fluid dynamics and defines baseline OCT-based patient profiles that associate 12-month visual outcomes in a large cohort of treated naïve nAMD eyes nationwide.

The purpose of this study was to evaluate specifically the relationship between glycated haemoglobin (HbA1c) levels and retinal optical coherence tomography (OCT) and OCT angiography (OCTA) parameters in type 1 Diabetes Mellitus (DM). A total of 478 type 1 DM patients and 115 controls were included in a prospective OCTA trial (ClinicalTrials.gov NCT03422965). Subgroup analysis was performed for controls, no diabetic retinopathy (DM-no DR) and DR patients (DM-DR), and HbA1c levels. OCT and OCTA measurements were compared with HbA1c levels (current and previous 5 years). DM-no DR patients with HbA1c levels >7.5% showed lower VD than DM-DR and controls (20.16 vs. 20.22 vs. 20.71, p < 0.05), and showed a significant correlation between HbA1c levels and FAZc (p = 0.04), after adjusting for age, gender, signal strength index, axial length, and DM disease duration. DM-DR patients with HbA1c > 7.5% presented greater CRT than DM-no DR and controls (270.8 vs. 260 vs. 251.1, p < 0.05) and showed a significant correlation between HbA1c and CRT (p = 0.03). In conclusion, greater levels of HbA1c are associated with OCTA changes in DM-no DR patients, and with structural OCT changes in DM-DR patients. The combination of OCTA and OCT measurements and HbA1c levels may be helpful to identify patients at risk of progression to greater stages of the diabetic microvascular disease.

Aim To evaluate the impact of fluid volume fluctuations quantified with artificial intelligence in optical coherence tomography scans during the maintenance phase and visual outcomes at 12 and 24 months in a real-world, multicentre, national cohort of treatment-naïve neovascular age-related macular degeneration (nAMD) eyes. Methods Demographics, visual acuity (VA) and number of injections were collected using the Fight Retinal Blindness tool. Intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), total fluid (TF) and central subfield thickness (CST) were quantified using the RetinAI Discovery tool. Fluctuations were defined as the SD of within-eye quantified values, and eyes were distributed according to SD quartiles for each biomarker. Results A total of 452 naïve nAMD eyes were included. Eyes with highest (Q4) versus lowest (Q1) fluid fluctuations showed significantly worse VA change (months 3–12) in IRF −3.91 versus 3.50 letters, PED −4.66 versus 3.29, TF −2.07 versus 2.97 and CST −1.85 versus 2.96 (all p<0.05), but not for SRF 0.66 versus 0.93 (p=0.91). Similar VA outcomes were observed at month 24 for PED −8.41 versus 4.98 (p<0.05), TF −7.38 versus 1.89 (p=0.07) and CST −10.58 versus 3.60 (p<0.05). The median number of injections (months 3–24) was significantly higher in Q4 versus Q1 eyes in IRF 9 versus 8, SRF 10 versus 8 and TF 10 versus 8 (all p<0.05). Conclusion This multicentre study reports a negative effect in VA outcomes of fluid volume fluctuations during the maintenance phase in specific fluid compartments, suggesting that anatomical and functional treatment response patterns may be fluid-specific.

Aim: To explore associations between artificial intelligence (AI)-based baseline optical coherence tomography (OCT) fluid compartment quantifications and 12-month visual outcomes in diabetic macular oedema (DME) eyes treated with the intravitreal dexamethasone implant. Methods: This was a multicentre, real-world, national DME database and associated OCT dataset study. Demographics, visual acuity (VA), treatments and visit data were collected using a validated web-based tool (Fight Retinal Blindness). Fluid compartment quantifications, including intraretinal fluid (IRF) and subretinal fluid (SRF), were measured in nanolitres (nL) using a validated AI tool (Discovery). Univariate and multivariate regression mixed models evaluated associations between anatomical variables and VA outcomes. Results: A total of 101 treatment-naïve DME eyes were grouped into quartiles according to their fluid volume for each fluid compartment (Q1: lowest volume, Q4: highest volume). Baseline IRF was associated with greater VA gains at month 12 (+6.34 letters, p=0.07) but poorer final VA (−8.95, p=0.07), while SRF was associated with worse final VA at 12 months (−12.5, p=0.01). At month 3, IRF was associated with a VA decrease at 12 months (−13.7, p=0.02) and lower final VA (−29.8, p<0.001). At month 12, IRF was associated with lower final VA (−11.6, p=0.03). Quantitatively, a reduction of 100 nL of IRF at 3 months was associated with a +1.54 letters gain (p=0.03) in the multivariate analysis. Conclusion: This real-world, multicentre study describes objective baseline fluid volumes that predict visual outcomes at 12 months in routine clinical care. Accurate quantification of baseline fluid volumes may play a predictive role for final visual outcomes.

Objectives: This study aimed to investigate the association between optical coherence tomography angiography (OCTA) parameters and cardiovascular (CV) risk scores in individuals with type 1 diabetes (T1D). Methods: A cross-sectional analysis of a large-scale prospective OCTA trial cohort (ClinicalTrials.gov NCT03422965) was performed. Demographic, systemic, and ocular data—including OCTA imaging—were collected. T1D participants were stratified into three CV risk categories: moderate (MR), high (HR), and very high risk (VHR). Individualized predictions for fatal and non-fatal CV events at 5 and 10 years were calculated using the STENO T1 Risk Engine calculator. Results: A total of 501 individuals (1 eye/patient; 397 T1D, 104 controls) were included. Subjects with MR (n = 37), HR (n = 152) and VHR (n = 208) exhibited significantly reduced vessel density (VD) (20.9 ± 1.3 vs. 20.2 ± 1.6 vs. 19.3 ± 1.8 mm⁻¹, p < 0.05), perfusion density (PD) (0.37 ± 0.02 vs. 0.36 ± 0.02 vs. 0.35 ± 0.02%, p < 0.05) and foveal avascular zone circularity (0.69 ± 0.06 vs. 0.65 ± 0.07 vs. 0.63 ± 0.09, p < 0.05). Statistically significant negative correlations were observed between CV risk and OCTA parameters including VD, PD, and retinal nerve fiber layer thickness, while central macular thickness (CMT) showed a positive correlation (p < 0.05). Notably, CMT was significantly associated with 5-year CV risk. Conclusions: OCTA-derived metrics, particularly reduced retinal VD and PD, are associated with elevated CV risk scores in T1D patients. These findings suggest that OCTA may serve as a valuable non-invasive tool for identifying individuals with increased CV risk scores.