Artificial intelligence-based fluid quantification and associated visual outcomes in a real-world, multicentre neovascular age-related macular degeneration national database.

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.

Macular Neovascularization Type Influence on Anti-VEGF Intravitreal Therapy Outcomes in Age-Related Macular Degeneration.

PURPOSE: To evaluate the influence of macular neovascularization (MNV) lesion type on 12-month clinical outcomes in treatment-naive eyes with neovascular age-related macular degeneration (nAMD) treated with anti-VEGF drugs nationwide. DESIGN: Multicenter national nAMD database observational study. SUBJECTS: One thousand six hundred six treatment-naive nAMD eyes (1330 patients) undergoing anti-VEGF therapy for 12 months nationwide. METHODS: Demographics, visual acuity (VA) in logarithm of the minimum angle of resolution letters, number of injections and visits were was collected using a validated web-based tool. Neovascular lesion phenotype was classified as type 1 (T1, n = 711), type 2 (T2, n = 505), type 3 (T3, n = 315), and aneurysmal type 1 (A-T1, n = 75), according to the new proposed consensus classification. MAIN OUTCOME MEASURES: Mean VA change at 12 months, final VA at 12 months, number of injections, time to lesion inactivation. RESULTS: A total of 1606 treatment-naive nAMD eyes (1330 patients) received a median of 7 injections over 12 months. Mean (± standard deviation) baseline VA was significantly lower for T2 (49.4 ± 23.5 letters) compared with T1 (57.8 ± 20.8) and T3 (58.2 ± 19.4) (both P < 0.05) lesions. Mean VA change at 12 months was significantly greater for A-T1 (+9.5 letters) compared with T3 (+3.1 letters, P < 0.05). Patients with T3 lesions had fewer active visits (24.9%) than those with other lesion types (T1, 30.5%; T2, 32.6%; A-T1, 27.5%; all P < 0.05). Aflibercept was the most used drug in A-T1 lesions (70.1%) and ranibizumab in T1 (40.7%), T2 (57.7%), and T3 (47.6%) lesions. CONCLUSIONS: This study highlights the relevance of MNV type on clinical outcomes in nAMD and reports significant differences in baseline VA, VA change, and lesion activity at 12 months. This report provides data about lesion-specific clinical features, which may guide the management of nAMD cases and potentially support personalized clinical decision making for these patients. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis.

Purpose: To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis. Design: Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965). Participants: Patients with type 1 DM and controls included in the progenitor study. Methods: Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types. Results: A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets. Conclusions: Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Optical Coherence Tomography Angiography in Type 1 Diabetes Mellitus. Report 4: Glycated Haemoglobin.

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.