RETINAL LEAKAGE INDEX DYNAMICS ON ULTRA-WIDEFIELD FLUORESCEIN ANGIOGRAPHY IN EYES TREATED WITH INTRAVITREAL AFLIBERCEPT FOR PROLIFERATIVE DIABETIC RETINOPATHY IN THE RECOVERY STUDY.

PURPOSE: Characterization of leakage indices on ultra-widefield fluorescein angiography in proliferative diabetic retinopathy treated with intravitreal aflibercept. METHODS: Prospective study enrolling subjects for treatment of proliferative diabetic retinopathy randomized 1:1 to receive 2-mg intravitreal aflibercept every 4 weeks (2q4) or every 12 weeks (2q12). Ultra-widefield fluorescein angiography images obtained at baseline, 24, and 48 weeks were analyzed using a semiautomated leakage segmentation platform. Panretinal and zonal leakage indices were calculated. RESULTS: Forty eyes of 40 subjects were included, and mean age was 48 ± 12.1 years. Mean number of injections was 11 ± 1.7 in the 2q4 arm and 4 ± 0.4 in the 2q12 arm. Median baseline leakage index in the 2q4 and 2q12 groups was 5.1% and 4.3%, respectively (P = 0.28). At 24 and 48 weeks, the 2q4 group significantly improved to 1.1% (-79%, P < 0.0001). At Week 24, the 2q12 group demonstrated nonsignificant improvement (3.4%; -21%, P = 0.47); by Week 48, improvement was significant (1.4%; -68%, P = 0.02). The 2q4 group resulted in lower leakage index compared with the 2q12 group at 24 weeks (1.1% vs. 3.4%, respectively; P = 0.008), but by 48 weeks, leakage index was similar between both groups (1.1% vs. 1.4%, respectively; P = 0.34). CONCLUSION: Proliferative diabetic retinopathy treated with intravitreal aflibercept demonstrated significant leakage index reductions at 1 year. Monthly dosing provided more rapid reduction in leakage index compared with quarterly dosing. TRIAL REGISTRATION: RECOVERY study (NCT02863354); https://clinicaltrials.gov/ct2/show/NCT02863354.

Quantitative Ultra-Widefield Angiography and Diabetic Retinopathy Severity: An Assessment of Panretinal Leakage Index, Ischemic Index and Microaneurysm Count.

PURPOSE: To investigate the relationship between the diabetic retinopathy (DR) severity and quantitative ultra-widefield angiographic metrics, including leakage index, ischemic index, and microaneurysm count. DESIGN: Retrospective image analysis study. METHODS: Eyes with DR that had undergone ultra-widefield fluorescein angiography (UWFA) with associated color photography were identified. All eyes were laser-naive and had not received any intravitreal pharmacotherapy within 6 months of UWFA. Each eye was graded for DR severity. Quantitative angiographic parameters were evaluated with a semiautomated analysis platform with expert reader correction, as needed. Angiographic parameters included panretinal leakage index, ischemic index, and microaneurysm count. Clinical characteristics analyzed included age, gender, race, hemoglobin A1C level, hypertension, systolic blood pressure, diastolic blood pressure, and smoking history. MAIN OUTCOME MEASURES: Association of DR severity with panretinal leakage index, ischemic index, and microaneurysm count. RESULTS: Three hundred thirty-nine eyes were included with mean age of 62±13 years. Forty-two percent of eyes were from women and 57.5% were from men. Distribution of DR severity was as follows: mild NPDR in 11.2%, moderate NPDR in 23.9%, severe NPDR in 40.1%, and PDR with 24.8%. Panretinal leakage index [mild NPDR (mean = 0.51%), moderate NPDR mean = 1.20%, severe NPDR (mean = 2.75%), and PDR (mean = 5.84%); P<2×10-16], panretinal ischemic index [mild NPDR (mean = 0.95%, moderate NPDR (mean = 1.37%), severe NPDR (mean = 2.80%), and PDR (mean = 9.53%); P<2×10-16], and panretinal microaneurysm count [mild NPDR (mean = 36), moderate NPDR (mean = 129), severe NPDR (mean = 203), and PDR (mean = 254); P<5×10-7] were strongly associated with DR severity. Multivariate analysis demonstrated that ischemic index and leakage index were the parameters associated most strongly with level of DR severity. CONCLUSIONS: Panretinal leakage index, panretinal ischemic index, and panretinal microaneurysm count are associated with DR severity. Additional research is needed to understand the clinical implications of these parameters related to progression risk, prognosis, and implications for therapeutic response.

Deep learning-enabled ultra-widefield retinal vessel segmentation with an automated quality-optimized angiographic phase selection tool.

OBJECTIVES: To demonstrate the feasibility of a deep learning-based vascular segmentation tool for UWFA and evaluate its ability to automatically identify quality-optimized phase-specific images. METHODS: Cumulative retinal vessel areas (RVA) were extracted from all available UWFA frames. Cubic splines were fitted for serial vascular assessment throughout the angiographic phases of eyes with diabetic retinopathy (DR), sickle cell retinopathy (SCR), or normal retinal vasculature. The image with maximum RVA was selected as the optimum early phase. A late phase frame was selected at a minimum of 4 min that most closely mirrored the RVA from the selected early image. Trained image analysts evaluated the selected pairs. RESULTS: A total of 13,980 UWFA sequences from 462 sessions were used to evaluate the performance and 1578 UWFA sequences from 66 sessions were used to create cubic splines. Maximum RVA was detected at a mean of 41 ± 15, 47 ± 27, 38 ± 8 s for DR, SCR, and normals respectively. In 85.2% of the sessions, appropriate images for both phases were successfully identified. The individual success rate was 90.7% for early and 94.6% for late frames. CONCLUSIONS: Retinal vascular characteristics are highly phased and field-of-view sensitive. Vascular parameters extracted by deep learning algorithms can be used for quality assessment of angiographic images and quality optimized phase selection. Clinical applications of a deep learning-based vascular segmentation and phase selection system might significantly improve the speed, consistency, and objectivity of UWFA evaluation.

Real-Time Photographic- and Fluorescein Angiographic-Guided Management of Diabetic Retinopathy: Randomized PRIME Trial Outcomes.

PURPOSE: To assess the safety and efficacy of as-needed (PRN) intravitreal aflibercept injections (IAI) in managing diabetic retinopathy (DR) guided by the real-time DR severity scale (DRSS) level or panretinal leakage index (PLI) assessment among eyes without diabetic macular edema (DME). DESIGN: Prospective, randomized phase 2 trial (PRIME). METHODS: A total of 40 eyes with nonproliferative (NPDR) or proliferative DR (PDR) received monthly IAIs until a DRSS improvement of ≥2 steps was achieved and eyes were randomized (1:1) to DRSS-guided or PLI-guided management strategies graded by a central reading center. Main outcome measurements included safety and changes in DRSS and PLI. RESULTS: Through week 52, 95% of eyes achieved a DRSS improvement of ≥2 steps. Following DRSS improvement, 97% of eyes required at least 1 PRN IAI. In eyes requiring PRN IAI and completing week 52, 100% and 59% experienced DRSS worsening (P = .01) in the DRSS- and PLI-guided arms, respectively. Through week 52, mean PLI decreased 18.2% (P = .49) and 54.6% (P <.0001), respectively, in the DRSS- and PLI-guided arms. NPDR versus PDR eyes at baseline achieved a DRSS improvement of ≥2 steps after a mean 4.9 and 3.6 IAIs (P = .03). Two eyes developed a PDR event at week 52 following 5 months of quiescence. CONCLUSIONS: The randomized PRIME study analyzed 2 imaging-based biomarkers to guide PRN management with IAI of DR without DME: DRSS level and PLI. Within the context of this study with limitations, most patients required IAI re-treatment every 3-4 months, and deterioration of PLI appeared to precede DRSS level worsening. Finally, these findings reaffirm the fact that close clinical follow-up is important even among eyes that achieve substantial DRSS improvements with apparently quiescent disease.

Longitudinal panretinal microaneurysm dynamics on ultra-widefield fluorescein angiography in eyes treated with intravitreal aflibercept for proliferative diabetic retinopathy in the recovery study.

BACKGROUND/AIMS: Quantifying microaneurysms (MAs) turnover may be an objective measure for therapeutic response in diabetic retinopathy. This study assesses changes in MA counts on ultra-widefield fluorescein angiography (UWFA) in subjects undergoing treatment with intravitreal aflibercept injection (IAI) for proliferative diabetic retinopathy (PDR) in the Intravitreal Aflibercept for Retinal Non-Perfusion in Proliferative Diabetic Retinopathy(RECOVERY) study using an automated MA detection platform. METHODS: RECOVERY is a prospective study that enrolled 40 subjects with PDR randomised 1:1 to receive 2 mg IAI every 4 weeks(q4wk) or every 12 weeks (q12wk). UWFA images were obtained at baseline, 6 months and 1 year. Images were analysed using an automated segmentation platform to detect and quantify MAs. Zones 1, 2 and 3 correspond to the macula, mid-periphery and far-periphery, respectively. RESULTS: The q4wk cohort demonstrated a significant decline in MAs in all zones and panretinally at baseline versus month 6, baseline versus year 1, and month 6 versus year 1 (-20.0% to -61.8%; all p<0.001). In the q12wk cohort, baseline versus month 6 showed a significant decline panretinally (mean: -34.2%; p<0.001) and in zone 3 (mean -44.18%; p<0.001). Addiitonally, baseline to year 1 in the q12wk group demonstrated significant decline panretinally (mean: -47.7%; p<0.001) and in zone 3 (mean: -59.8%; p<0.001). All zones demonstrated significantly decline from month 6 to year 1 in the q12wk group. CONCLUSION: Therapy with IAI demonstrates significantly reduced panretinal MA counts in PDR at 1 year in both treatment groups. The use of automated platforms to detect and quantify MAs may provide a novel imaging marker for evaluating disease activity and therapeutic impact. TRIAL REGISTRATION NUMBER: NCT02863354.

Automated Quality Assessment and Image Selection of Ultra-Widefield Fluorescein Angiography Images through Deep Learning.

Purpose: Numerous angiographic images with high variability in quality are obtained during each ultra-widefield fluorescein angiography (UWFA) acquisition session. This study evaluated the feasibility of an automated system for image quality classification and selection using deep learning. Methods: The training set was comprised of 3543 UWFA images. Ground-truth image quality was assessed by expert image review and classified into one of four categories (ungradable, poor, good, or best) based on contrast, field of view, media opacity, and obscuration from external features. Two test sets, including randomly selected 392 images separated from the training set and an independent balanced image set composed of 50 ungradable/poor and 50 good/best images, assessed the model performance and bias. Results: In the randomly selected and balanced test sets, the automated quality assessment system showed overall accuracy of 89.0% and 94.0% for distinguishing between gradable and ungradable images, with sensitivity of 90.5% and 98.6% and specificity of 87.0% and 81.5%, respectively. The receiver operating characteristic curve measuring performance of two-class classification (ungradable and gradable) had an area under the curve of 0.920 in the randomly selected set and 0.980 in the balanced set. Conclusions: A deep learning classification model demonstrates the feasibility of automatic classification of UWFA image quality. Clinical application of this system might greatly reduce manual image grading workload, allow quality-based image presentation to clinicians, and provide near-instantaneous feedback on image quality during image acquisition for photographers. Translational Relevance: The UWFA image quality classification tool may significantly reduce manual grading for clinical- and research-related work, providing instantaneous and reliable feedback on image quality.