To assess the potential of machine learning to predict low and high treatment demand in real life in patients with neovascular age-related macular degeneration (nAMD), retinal vein occlusion (RVO), and diabetic macular edema (DME) treated according to a treat-and-extend regimen (TER). Retrospective cohort study. Three hundred seventy-seven eyes (340 patients) with nAMD and 333 eyes (285 patients) with RVO or DME treated with anti-vascular endothelial growth factor agents (VEGF) according to a predefined TER from 2014 through 2018. Eyes were grouped by disease into low, moderate, and high treatment demands, defined by the average treatment interval (low, ≥10 weeks; high, ≤5 weeks; moderate, remaining eyes). Two random forest models were trained to predict the probability of the long-term treatment demand of a new patient. Both models use morphological features automatically extracted from the OCT volumes at baseline and after 2 consecutive visits, as well as patient demographic information. Evaluation of the models included a 10-fold cross-validation ensuring that no patient was present in both the training set (nAMD, approximately 339; RVO and DME, approximately 300) and test set (nAMD, approximately 38; RVO and DME, approximately 33). Mean area under the receiver operating characteristic curve (AUC) of both models; contribution to the prediction and statistical significance of the input features. Based on the first 3 visits, it was possible to predict low and high treatment demand in nAMD eyes and in RVO and DME eyes with similar accuracy. The distribution of low, high, and moderate demanders was 127, 42, and 208, respectively, for nAMD and 61, 50, and 222, respectively, for RVO and DME. The nAMD-trained models yielded mean AUCs of 0.79 and 0.79 over the 10-fold crossovers for low and high demand, respectively. Models for RVO and DME showed similar results, with a mean AUC of 0.76 and 0.78 for low and high demand, respectively. Even more importantly, this study revealed that it is possible to predict low demand reasonably well at the first visit, before the first injection. Machine learning classifiers can predict treatment demand and may assist in establishing patient-specific treatment plans in the near future.

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.

Purpose: Diabetic retinopathy (DR) is the leading cause of vision impairment in working-age adults. Automated screening can increase DR detection at early stages at relatively low costs. We developed and evaluated a cloud-based screening tool that uses artificial intelligence (AI), the LuxIA algorithm, to detect DR from a single fundus image. Methods: Color fundus images that were previously graded by expert readers were collected from the Canarian Health Service (Retisalud) and used to train LuxIA, a deep-learning–based algorithm for the detection of more than mild DR. The algorithm was deployed in the Discovery cloud platform to evaluate each test set. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were computed using a bootstrapping method to evaluate the algorithm performance and compared through different publicly available datasets. A usability test was performed to assess the integration into a clinical tool. Results: Three separate datasets, Messidor-2, APTOS, and a holdout set from Retisalud were evaluated. Mean sensitivity and specificity with 95% confidence intervals (CIs) reached for these three datasets were 0.901 (0.901–0.902) and 0.955 (0.955–0.956), 0.995 (0.995–0.995) and 0.821 (0.821–0.823), and 0.911 (0.907–0.912) and 0.880 (0.879–0.880), respectively. The usability test confirmed the successful integration of LuxIA into Discovery. Conclusions: Clinical data were used to train the deep-learning–based algorithm LuxIA to an expert-level performance. The whole process (image uploading and analysis) was integrated into the cloud-based platform Discovery, allowing more patients to have access to expert-level screening tools. Translational Relevance: Using the cloud-based LuxIA tool as part of a screening program may give diabetic patients greater access to specialist-level decisions, without the need for consultation.

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.

To compare drusen volume between Heidelberg Spectral Domain (SD-) and Zeiss Swept-Source (SS) PlexElite Optical Coherence Tomography (OCT) determined by manual and automated segmentation methods. Thirty-two eyes of 24 patients with Age-Related Macular Degeneration (AMD) and drusen maculopathy were included. In the central 1 and 3 mm ETDRS circle drusen volumes were calculated and compared. Drusen segmentation was performed using automated manufacturer algorithms of the two OCT devices. Then, the automated segmentation was manually corrected and compared and finally analyzed using customized software. Though on SD-OCT, there was a significant difference of mean drusen volume prior to and after manual correction (mean difference: 0.0188 ± 0.0269 mm³, p < 0.001, corr. p < 0.001, correlation of r = 0.90), there was no difference found on SS-OCT (mean difference: 0.0001 ± 0.0003 mm³, p = 0.262, corr. p = 0.524, r = 1.0). Heidelberg-acquired mean drusen volume after manual correction was significantly different from Zeiss-acquired drusen volume after manual correction (mean difference: 0.1231 ± 0.0371 mm³, p < 0.001, corr. p < 0.001, r = 0.68). Using customized software, the difference of measurements between both devices decreased and correlation among the measurements improved (mean difference: 0.0547 ± 0.0744 mm³, p = 0.02, corr. p = 0.08, r = 0.937). Heidelberg SD-OCT, the Zeiss PlexElite SS-OCT, and customized software all measured significantly different drusen volumes. Therefore, devices/algorithms may not be interchangeable. Third-party customized software helps to minimize differences, which may allow a pooling of data of different devices, e.g., in multicenter trials.

To assess potential optical coherence tomography (OCT) biomarkers associated with the presence and future development of secondary choroidal neovascularization (CNV) in inflammatory lesions using artificial intelligence (AI)-based segmentation in patients with punctate inner choroidopathy (PIC) and multifocal choroiditis (MFC). Multicenter, retrospective cross-sectional and cohort study. The study included 208 eyes from 156 patients, comprising 112 patients with PIC (143 eyes) and 44 patients with MFC (65 eyes). Sequential OCT scans from patients with MFC and PIC were analyzed using an AI software tool (Discovery OCT Biomarker Detector, RetinAI AG, Switzerland). The software evaluated morphologic biomarkers, fluid and lesion compartment volumes, and retinal layer thickness. Manual corrections were performed on AI-based segmentations when necessary. Data were compared between MFC and PIC, and between eyes with and without CNV presence and development. Linear mixed effects modeling was employed to identify predictive biomarkers for CNV development. The primary outcomes were baseline lesion volume and presence of CNV, and the secondary outcome was best-corrected visual acuity (BCVA). At baseline, CNV was present in 72.6% of the eyes (75.5% in PIC and 66.1% in MFC). During follow-up, 20 eyes developed CNV from inflammatory lesions (16 with PIC and 4 with MFC). CNV-positive eyes had significantly lower BCVA at baseline (0.28 ± 0.26 logMAR vs 0.14 ± 0.21 logMAR; P < .0001). Baseline lesion volume was significantly greater in CNV-positive eyes (33.80 ± 63.71 nL) compared with CNV-negative eyes (5.44 ± 12.44 nL; P < .0001). Larger lesion volume (β = 24.29, 95% CI 9.77-38.80; P = .001) and longer disease duration (β = 0.17, 95% CI 0.07-0.27; P = .001) were associated with CNV presence. Larger baseline lesion volume and longer disease duration are associated with secondary CNV in PIC and MFC patients. AI-driven OCT analysis has the potential to identify key biomarkers, supporting personalized treatment strategies and improving patient outcomes.