Application of Automated Quantification of Fluid Volumes to Anti–VEGF Therapy of Neovascular Age-Related Macular Degeneration

Association between three-dimensional morphological features and functional indicators of neovascular age-related macular degeneration

PURPOSE

To investigate the correlation between morphological lesions and functional indicators in eyes with neovascular age-related macular degeneration (nAMD).

METHODS

This was a prospective observational study of treatment-naïve nAMD eyes. Various morphological lesions and impaired retinal structures were manually measured at baseline and month-3 in three-dimensional optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images, including the volumes (mm3) of macular neovascularization (MNV), avascular subretinal hyperreflective material (avascular SHRM), subretinal fluid (SRF), intraretinal fluid (IRF), serous pigment epithelial detachment (sPED) and the impaired area (mm2) of ellipsoid zone (EZ), external limiting membrane (ELM) and outer nuclear layer (ONL).

RESULTS

Sixty-three eyes were included. The volume of avascular SHRM showed persistent positive associations with the area of EZ damage, both at baseline, month-3, and change values (all P < 0.001). Poor BCVA (month-3) was associated with larger volumes of baseline IRF (β = 0.377, P < 0.001), avascular SHRM (β = 0.306, P = 0.032), and ELM impairment area (β = 0.301, P = 0.036) in multivariate model. EZ and ELM impairment were primarily associated with baseline avascular SHRM (β = 0.374, p = 0.003; β = 0.388, P < 0.001, respectively), while ONL impairment primarily associated with MNV (β = 0.475, P < 0.001).

CONCLUSION

The utilization of three-dimensional measurements elucidates the intrinsic connections among various lesions and functional outcomes. In particular, avascular SHRM plays an important role in prognosis of nAMD.

Faricimab in Neovascular AMD Complicated by Pigment Epithelium Detachment: An AI-Assisted Evaluation of Early Morphological Changes

INTRODUCTION

This study investigates the early temporal changes in pigment epithelial detachment (PED) morphology following treatment with faricimab in patients with neovascular age-related macular degeneration (nAMD). Utilizing an artificial intelligence (AI)-assisted approach, we provide a detailed quantification and characterization of the dynamics of these morphological changes.

METHODS

A prospective observational study was conducted on 22 eyes from 22 treatment-naïve patients with nAMD-associated PED (presenting either type 1 or type 3 macular neovascularization). Participants were administered intravitreal faricimab (6 mg) at baseline and at days 30, 60, and 90. Comprehensive ophthalmic evaluations and spectral-domain optical coherence tomography (SD-OCT) imaging were conducted at baseline and at seven additional follow-up visits on days 1, 7, 14, 30, 60, 90, and 120. An AI-based automated segmentation algorithm was utilized to precisely quantify changes in PED volume, alongside intraretinal (IRF) and subretinal fluid (SRF) volumes, at each time point.

RESULTS

Treatment with faricimab resulted in a significant reduction in mean PED volume, with an average decrease of 12% at day 1, 29% at day 7, 51% at day 14, 68% at day 30, 72% at day 60, 79% at day 90, and 84% at day 120 (p < 0.0001 for all time points). Similarly rapid and marked reductions were noted in both mean IRF (23.5% at day 1, 90.7% at day 14) and SRF (14.4% at day 1, 91.2% at day 14) volumes. The study also showed a statistically significant improvement in best-corrected visual acuity (BCVA) over the follow-up period, correlating with the reduction in PED volume.

CONCLUSION

Faricimab demonstrates early and significant efficacy in improving PED architecture in patients with nAMD. The rapid morphological improvements observed in this study suggest faricimab may represent a valid therapeutic option for PEDs associated with nAMD.

An Automated Comparative Analysis of the Exudative Biomarkers in Neovascular Age-Related Macular Degeneration, The RAP Study: Report 6

PURPOSE

To investigate differences in volume and distribution of the main exudative biomarkers across all types and subtypes of macular neovascularization (MNV) using artificial intelligence (AI).

DESIGN

Cross-sectional study.

METHODS

An AI-based analysis was conducted on 34,528 OCT B-scans consisting of 281 (250 unifocal, 31 multifocal) MNV3, 55 MNV2, and 121 (30 polypoidal, 91 non-polypoidal) MNV1 treatment-naive eyes. Means (SDs), medians and heat maps of cystic intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachments (PED), and hyperreflective foci (HRF) volumes, as well as retinal thickness (RT) were compared among MNV types and subtypes.

RESULTS

MNV3 had the highest mean IRF with 291 (290) nL, RT with 357 (49) µm, and HRF with 80 (70) nL, P ≤ .05. MNV1 showed the greatest mean SRF with 492 (586) nL, whereas MNV3 exhibited the lowest with 218 (382) nL, P ≤ .05. Heat maps showed IRF confined to the center, whereas SRF was scattered in all types. SRF, HRF, and PED were more distributed in the temporal macular half in MNV3. Means of IRF, HRF, and PED were higher in the multifocal than in the unifocal MNV3 with 416 (309) nL,114 (95) nL, and 810 (850) nL, P ≤ .05. Compared to the non-polypoidal subtype, the polypoidal subtype had greater means of SRF with 695 (718) nL, HRF 69 (63) nL, RT 357 (45) µm, and PED 1115 (1170) nL, P ≤ .05.

CONCLUSIONS

This novel quantitative AI analysis shows that SRF is a biomarker of choroidal origin in MNV1, whereas IRF, HRF, and RT are retinal biomarkers in MNV3. Polypoidal MNV1 and multifocal MNV3 present with higher exudation compared to other subtypes.

Impact of residual retinal fluid on treatment outcomes in neovascular age-related macular degeneration

Treatment decisions for neovascular age-related macular degeneration (nAMD) in the setting of individualised treatment regimens are adapted to disease activity. The main marker of disease activity and trigger for re-treatment with anti-vascular endothelial growth factor (anti-VEGF) agents is the presence of retinal fluid on optical coherence tomography (OCT). Recently, attention has focused on the impact of residual retinal fluid on nAMD management. Based on a literature review and the combined clinical experience of an international group of retinal specialists, this manuscript provides expert guidance on the treatment of nAMD according to fluid status and proposes an algorithm for determining when to administer anti-VEGF treatment according to residual fluid status. We explore the role of residual fluid in treatment decisions and outcomes in nAMD, taking into consideration fluid evaluation and, in particular, distinguishing between fluid in different anatomic compartments and at different stages during the treatment course. Current limitations to identifying and interpreting fluid on OCT, and the assumption that any residual retinal fluid reflects ongoing VEGF activity, are discussed.

Compartmental Exudative Dynamics in Neovascular Age-Related Macular Degeneration: Volumetric Outcomes and Impact of Volatility in a Phase III Clinical Trial

PURPOSE

To examine retinal feature dynamics in eyes with neovascular age-related macular degeneration (nAMD) treated with anti-VEGF therapy and the relationship of these features with visual acuity.

DESIGN

Post hoc analysis of the phase III, randomized, HAWK nAMD clinical trial.

PARTICIPANTS

Participants randomized to the brolucizumab 6 mg or aflibercept 2 mg arms of the trial.

METHODS

Spectral-domain OCT scans collected at 4-week intervals were analyzed using an automated machine learning-enhanced segmentation and feature-extraction platform with manual verification. Quantitative volumetric measures of retinal and exudative features were exported at multiple timepoints over 48 weeks. Volatility of exudative features was calculated as the standard deviation of each feature value during the maintenance phase (week 12-48) of treatment. These features were examined for their associations with anatomic and functional outcomes.

MAIN OUTCOME MEASURES

Longitudinal intraretinal fluid (IRF) and subretinal fluid (SRF) volume, subretinal hyperreflective material (SHRM) volume, ellipsoid zone (EZ) integrity (EZ-retinal pigment epithelium [RPE] volume/thickness), and correlation with best-corrected visual acuity (BCVA).

RESULTS

Intraretinal fluid, SRF, and SHRM demonstrated significant volumetric reduction from baseline with anti-VEGF therapy (P < 0.001 at each timepoint). Ellipsoid zone integrity measures demonstrated significant improvement from baseline (P < 0.001 at each timepoint). Both EZ integrity and SHRM measures correlated significantly with BCVA at all timepoints (EZ-RPE volume: 0.38 ≤ r ≤ 0.47; EZ-RPE central subfield thickness: 0.22 ≤ r ≤ 0.41; SHRM volume: -0.33 ≤ r ≤ -0.44). After treatment initiation, correlations of IRF and SRF volume with BCVA were weak or nonsignificant. Eyes with lower volatility of IRF, SRF, and SHRM volumes during the maintenance phase showed greater improvements in EZ integrity (all P < 0.01) and greater gains in BCVA (all P < 0.01) at week 48 compared with eyes with higher volatility in those exudative parameters.

CONCLUSIONS

Quantitative measures of SHRM volume and EZ integrity correlated more strongly with BCVA than retinal fluid volumes during treatment. High volatility of exudative parameters, including SRF, during the maintenance phase of treatment was associated with loss of EZ integrity and BCVA.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Effect of baseline fluid localization on visual acuity and prognosis in type 1 macular neovascularization treated with anti-VEGF

PURPOSE

To assess the prognostic value of subretinal (SRF) and intraretinal fluid (IRF) localizations in type 1 macular neovascularization (MNV) due to age-related macular degeneration (AMD).

SUBJECTS

Eyes were prospectively treated with anti-vascular epithelial growth factor (anti-VEGF) intravitreal injections (IVT) according to a Pro-Re-Nata (PRN) or Treat and Extend (TAE) regimen during 24 months. A total of 211 eyes with treatment-naïve type 1 MNV secondary to AMD were consecutively included. Eyes were divided between 2 groups according to the fluid localization: presence of SRF alone (SRF group), or presence of IRF associated or not with SRF (IRF ± SRF group).

RESULTS

At baseline the mean BCVA was 66.2 letters. SRF was present in 94.8% of eyes, IRF in 30.8%, and both in 25.6%. Data were available for 201 eyes at 12 months, and 157 eyes at 24 months. The presence of IRF at baseline was associated with lower baseline BCVA and significantly lower BCVA at 12 months (p < 0.001) and 24 months (p < 0.001). Eyes with SRF alone displayed better visual outcomes (BCVA at month 12, SRF = 74.3 letters, IRF ± SRF = 56.9 letters). In the presence of baseline IRF, fibrosis (p = 0.03) and atrophy (p < 0.001) were more frequently found at 24 months. In a multivariate model, the presence of baseline IRF was significantly associated with lower BCVA at month 12 but not at month 24.

CONCLUSION

In type 1 MNV, the presence of baseline IRF was associated with worse visual outcomes compared to SRF alone, and more frequent atrophy and fibrosis.

Retinal and choroidal changes after anti-VEGF therapy in neovascular-AMD patients: A systematic review and meta-analysis of SD-OCT studies

BACKGROUND

In recent years, the progress made in the field of optical coherence tomography has helped to understand the changes in eye layers in patients with exudative age-related macular degeneration (nAMD). Early diagnosis of nAMD, a leading cause of irreversible vision impairment, is helpful. Therefore, we performed a meta-analysis on OCT measurement alterations before and after anti-VEGF therapy in patients with nAMD and controls.

METHOD

We systematically searched Scopus, PubMed, Cochrane, and Web of Science to find articles that measured choroidal and retinal layer changes after anti-VEGF therapy in nAMD Patients. We chose either a fixed-effects or random-effects model based on the assessed heterogeneity level to perform a meta-analysis. In addition, we conducted meta-regression, subgroup analyses, publication bias, and quality assessment for included studies.

RESULTS

Thirteen studies were included in the meta-analysis, with 733 total participants. Foveal thickness and subfoveal choroidal thickness (CT) decreased significantly in the first 3 years after injections, except for subfoveal CT in the third year after injection. It also showed that CT at 1500 µm temporal and nasal to the fovea did not significantly change.

CONCLUSION

Our results showed anti-VEGF treatment for nAMD patients was associated with a significant reduction in foveal thickness and subfoveal CT in the first 2 years after treatment. Our analysis did not reveal any correlation between changes in foveal thickness and subfoveal CT with best-corrected visual acuity or other factors.

Approved AI-based fluid monitoring to identify morphological and functional treatment outcomes in neovascular age-related macular degeneration in real-world routine

AIM

To predict antivascular endothelial growth factor (VEGF) treatment requirements, visual acuity and morphological outcomes in neovascular age-related macular degeneration (nAMD) using fluid quantification by artificial intelligence (AI) in a real-world cohort.

METHODS

Spectral-domain optical coherence tomography data of 158 treatment-naïve patients with nAMD from the Fight Retinal Blindness! registry in Zurich were processed at baseline, and after initial treatment using intravitreal anti-VEGF to predict subsequent 1-year and 4-year outcomes. Intraretinal and subretinal fluid and pigment epithelial detachment volumes were segmented using a deep learning algorithm (Vienna Fluid Monitor, RetInSight, Vienna, Austria). A predictive machine learning model for future treatment requirements and morphological outcomes was built using the computed set of quantitative features.

RESULTS

Two hundred and two eyes from 158 patients were evaluated. 107 eyes had a lower median (≤7) and 95 eyes had an upper median (≥8) number of injections in the first year, with a mean accuracy of prediction of 0.77 (95% CI 0.71 to 0.83) area under the curve (AUC). Best-corrected visual acuity at baseline was the most relevant predictive factor determining final visual outcomes after 1 year. Over 4 years, half of the eyes had progressed to macular atrophy (MA) with the model being able to distinguish MA from non-MA eyes with a mean AUC of 0.70 (95% CI 0.61 to 0.79). Prediction for subretinal fibrosis reached an AUC of 0.74 (95% CI 0.63 to 0.81).

CONCLUSIONS

The regulatory approved AI-based fluid monitoring allows clinicians to use automated algorithms in prospectively guided patient treatment in AMD. Furthermore, retinal fluid localisation and quantification can predict long-term morphological outcomes.

Deep Learning in Neovascular Age-Related Macular Degeneration

Background and objectives: Age-related macular degeneration (AMD) is a complex and multifactorial condition that can lead to permanent vision loss once it progresses to the neovascular exudative stage. This review aims to summarize the use of deep learning in neovascular AMD. Materials and Methods: Pubmed search. Results: Deep learning has demonstrated effectiveness in analyzing structural OCT images in patients with neovascular AMD. This review outlines the role of deep learning in identifying and measuring biomarkers linked to an elevated risk of transitioning to the neovascular form of AMD. Additionally, deep learning techniques can quantify critical OCT features associated with neovascular AMD, which have prognostic implications for these patients. Incorporating deep learning into the assessment of neovascular AMD eyes holds promise for enhancing clinical management strategies for affected individuals. Conclusion: Several studies have demonstrated effectiveness of deep learning in assessing neovascular AMD patients and this has a promising role in the assessment of these patients.

Visual Outcome and Fluid Changes Between Eyes With Polypoidal Choroidal Vasculopathy Receiving Biosimilar CKD-701 or Reference Ranibizumab Therapy: A Post Hoc Analysis of a Phase 3 Randomized Clinical Trial

PURPOSE

To compare the visual outcome and fluid features of a proposed biosimilar, CKD-701, versus the reference ranibizumab in eyes with polypoidal choroidal vasculopathy (PCV).

METHODS

This was a post hoc analysis of a phase 3 randomized clinical trial assessing the efficacy and safety of CKD-701 and ranibizumab. A total of 73 PCV eyes were assigned randomly to either CKD-701 (36 eyes) or ranibizumab (37 eyes). The mean changes in best-corrected visual acuity (BCVA), central retinal thickness (CRT), pigment epithelial detachment (PED) volume, and fluid features were compared.

RESULTS

After three loading injections, the mean change in BCVA (letters) was +7.50 in the CKD-701 group and +6.32 in the ranibizumab group (p = .447). The changes in CRT and PED volume of the CKD-701 group (-107.25 ± 102.66 μm and -0.22 ± 0.46 mm3) were similar to those of the ranibizumab group (-96.78 ± 105.00 μm and -0.23 ± 0.54 mm3) (p = .668 and p = .943, respectively). Proportions of eyes with subretinal, intraretinal and sub-retinal pigment epithelium (RPE) fluids after three loading injections were not different between CKD-701 group (33.3%, 13.9% and 42.9%) and ranibizumab group (51.4%, 16.2% and 40.0%) (p = .071, p = 1.000 and p = .808). The visual and anatomical changes were similar between two groups at month 6 and 12 (all, p > .05).

CONCLUSION

Biosimilar CKD-701 monotherapy resulted in comparable visual and anatomical changes to those achieved with reference ranibizumab in PCV eyes.

Double-dose investigation of aflibercept in neovascular age-related macular degeneration (DIANA): a real-world study

BACKGROUND

To investigate the clinical effects of double-dose (4 mg) aflibercept treatment in neovascular age-related macular degeneration (nAMD), compared with the standard-dose (2 mg) treatment.

METHODS

A total of 108 eyes from 97 patients with nAMD and received intravitreal aflibercept 2 mg and/or 4 mg treatment were retrospectively reviewed. The changes of central macular thickness (CMT)/ pigmental epithelium detachment height and the recurrence rate of exudation during the 12-month follow-up were compared between the 2 mg group and the 4 mg group. Self-control comparisons (2 mg switch to 4 mg) were also made between two regimens.

RESULTS

Compared with the 2 mg group, tendencies of lower intraretinal fluid incidence and more CMT reduction were observed in the 4 mg group. The later one was also observed when eyes switching from 2 mg to 4 mg regimen. The median remission interval was 5 months in the 4 mg group, 2 months longer than the 3 months in the 2 mg group (P = 0.452). Injections needed in the 4 mg group were 3.644 ± 1.670, less than the 4.286 ± 2.334 injections in the 2 mg group within 12 months as well (P = 0.151). However, no associated vision benefits were gained from the double-douse regimen. No markedly increased-intraocular pressure events, or other adverse events were found in two groups.

CONCLUSIONS

Compared to the aflibercept 2 mg treatment in nAMD, tendencies of anatomic gains and relieving treatment burden were brought by the aflibercept 4 mg treatment. This study may have additional importance, given the further application of high-dose aflibercept in real-world settings.

A Systematic Prospective Comparison of Fluid Volume Evaluation across OCT Devices Used in Clinical Practice

Objective

Treatment decisions in neovascular age-related macular degeneration (nAMD) are mainly based on subjective evaluation of OCT. The purpose of this cross-sectional study was to provide a comparison of qualitative and quantitative differences between OCT devices in a systematic manner.

Design

Prospective, cross-sectional study.

Subjects

One hundred sixty OCT volumes, 40 eyes of 40 patients with nAMD.

Methods

Patients from clinical practice were imaged with 4 different OCT devices during one visit: (1) Spectralis Heidelberg; (2) Cirrus; (3) Topcon Maestro2; and (4) Topcon Triton. Intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED) were manually annotated in all cubes by trained human experts to establish fluid measurements based on expert-reader annotations. Intraretinal fluid, SRF, and PED volume were quantified in nanoliters (nL). Bland-Altman plots were created to analyze the agreement of measurements in the central 1 and 6 mm. The Friedman test was performed to test for significant differences in the central 1, 3, and 6 mm.

Main Outcome Measures

Intraretinal fluid, SRF, and PED volume.

Results

In the central 6 mm, there was a trend toward higher IRF and PED volumes in Spectralis images compared with the other devices and no differences in SRF volume. In the central 1 mm, the standard deviation of the differences ranged from ± 3 nL to ± 6 nL for IRF, from ± 3 nL to ± 4 nL for SRF, and from ± 7 nL to ± 10 nL for PED in all pairwise comparisons. Manually annotated IRF and SRF volumes showed no significant differences in the central 1 mm.

Conclusions

Fluid volume quantification achieved excellent reliability in all 3 retinal compartments on images obtained from 4 OCT devices, particularly for clinically relevant IRF and SRF values. Although fluid volume quantification is reliable in all 4 OCT devices, switching OCT devices might lead to deviating fluid volume measurements with higher agreement in the central 1 mm compared with the central 6 mm, with highest agreement for SRF volume in the central 1 mm. Understanding device-dependent differences is essential for expanding the interpretation and implementation of pixel-wise fluid volume measurements in clinical practice and in clinical trials.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Deep learning assisted fluid volume calculation for assessing anti-vascular endothelial growth factor effect in diabetic macular edema

Objective

To develop an algorithm using deep learning methods to calculate the volume of intraretinal and subretinal fluid in optical coherence tomography (OCT) images for assessing diabetic macular edema (DME) patients' condition changes.

Design

Cross-sectional study.

Participants

Treatment-naive patients diagnosed with DME recruited from April 2020 to November 2021.

Methods

The deep learning network, which was built for autonomous segmentation utilizing an encoder-decoder network based on the U-Net architecture, was used to calculate the volume of intraretinal fluid (IRF) and subretinal fluid (SRF). The alterations of retinal vessel density and thickness, and the correlation between best-corrected visual acuity (BCVA) and OCT parameters were analyzed.

Results

2,955 OCT images of fourteen eyes from DME patients with IRF and SRF who received anti-vascular endothelial growth factor (VEGF) agents were obtained. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the algorithm was 0.993 for IRF and 0.998 for SRF. The volumes of IRF and SRF were significantly decreased from 1.93 ± 0.58 /1.14 ± 0.25 mm3 (baseline) to 0.26 ± 0.13 /0.26 ± 0.18 mm3 (post-injection), respectively (p = 0.0170 for IRF, and p = 0.0004 for SRF). The Spearman correlation demonstrated that the reduction of IRF volume was negatively correlated with age (coefficient = -0.698, p = 0.006).

Conclusion

We developed a deep learning assisted fluid volume calculation algorithm with high sensitivity and specificity for assessing the volume of IRF and SRF in DME patients. Key words: deep learning; diabetic macular edema; optical coherence tomography.

AI-based support for optical coherence tomography in age-related macular degeneration

Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects.

Progress in clinical research and applications of retinal vessel quantification technology based on fundus imaging

Retinal blood vessels are the only directly observed blood vessels in the body; changes in them can help effective assess the occurrence and development of ocular and systemic diseases. The specificity and efficiency of retinal vessel quantification technology has improved with the advancement of retinal imaging technologies and artificial intelligence (AI) algorithms; it has garnered attention in clinical research and applications for the diagnosis and treatment of common eye and related systemic diseases. A few articles have reviewed this topic; however, a summary of recent research progress in the field is still needed. This article aimed to provide a comprehensive review of the research and applications of retinal vessel quantification technology in ocular and systemic diseases, which could update clinicians and researchers on the recent progress in this field.

Deep Learning-Based Retinal Layer Segmentation in Optical Coherence Tomography Scans of Patients with Inherited Retinal Diseases

BACKGROUND

In optical coherence tomography (OCT) scans of patients with inherited retinal diseases (IRDs), the measurement of the thickness of the outer nuclear layer (ONL) has been well established as a surrogate marker for photoreceptor preservation. Current automatic segmentation tools fail in OCT segmentation in IRDs, and manual segmentation is time-consuming.

METHODS AND MATERIAL

Patients with IRD and an available OCT scan were screened for the present study. Additionally, OCT scans of patients without retinal disease were included to provide training data for artificial intelligence (AI). We trained a U-net-based model on healthy patients and applied a domain adaption technique to the IRD patients' scans.

RESULTS

We established an AI-based image segmentation algorithm that reliably segments the ONL in OCT scans of IRD patients. In a test dataset, the dice score of the algorithm was 98.7%. Furthermore, we generated thickness maps of the full retinal thickness and the ONL layer for each patient.

CONCLUSION

Accurate segmentation of anatomical layers on OCT scans plays a crucial role for predictive models linking retinal structure to visual function. Our algorithm for segmentation of OCT images could provide the basis for further studies on IRDs.

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.

Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD

PURPOSE

To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated measurements with central retinal subfield thickness (CSFT) and fluid volume values.

METHODS

We utilized an automated deep learning approach to quantify macular fluid in SD-OCT volumes (Cirrus, Spectralis, Topcon) from patients of HAWK and HARRIER Studies. Three-dimensional volumes for IRF and SRF were measured at baseline and under therapy in the central millimeter and compared to fluid gradings, CSFT and foveal centerpoint thickness (CPT) values measured by the Vienna Reading Center.

RESULTS

41.906 SD-OCT volume scans were included into the analysis. Concordance between human expert grading and automated algorithm performance reached AUC values of 0.93/0.85 for IRF and 0.87 for SRF in HARRIER/HAWK in the central millimeter. IRF volumes showed a moderate correlation with CSFT at baseline (HAWK: r = 0.54; HARRIER: r = 0.62) and weaker correlation under therapy (HAWK: r = 0.44; HARRIER: r = 0.34). SRF and CSFT correlations were low at baseline (HAWK: r = 0.29; HARRIER: r = 0.22) and under therapy (HAWK: r = 0.38; HARRIER: r = 0.45). The residual standard error (IRF: 75.90 µm; SRF: 95.26 µm) and marginal residual standard deviations (IRF: 46.35 µm; SRF: 44.19 µm) of fluid volume were high compared to the range of CSFT values.

CONCLUSION

Deep learning-based segmentation of retinal fluid performs reliably on OCT images. CSFT values are weak indicators for fluid activity in nAMD. Automated quantification of fluid types, highlight the potential of deep learning-based approaches to objectively monitor anti-VEGF therapy.

Long-term effect of fluid volumes during the maintenance phase in neovascular age-related macular degeneration: results from Fight Retinal Blindness!

OBJECTIVE

To investigate the effect of macular fluid volumes (subretinal fluid [SRF], intraretinal fluid [IRF], and pigment epithelium detachment [PED]) after initial treatment on functional and structural outcomes in neovascular age-related macular degeneration in a real-world cohort from Fight Retinal Blindness!

METHODS

Treatment-naive neovascular age-related macular degeneration patients from Fight Retinal Blindness! (Zürich, Switzerland) were included. Macular fluid on optical coherence tomography was automatically quantified using an approved artificial intelligence algorithm. Follow-up of macular fluid, number of anti-vascular endothelial growth factor treatments, effect of fluid volumes after initial treatment (high, top 25%; low, bottom 75%) on best-corrected visual acuity, and development of macular atrophy and fibrosis was investigated over 48 months.

RESULTS

A total of 209 eyes (mean age, 78.3 years) were included. Patients with high IRF volumes after initial treatment differed by -2.6 (p = 0.021) and -7.4 letters (p = 0.007) at months 12 and 48, respectively. Eyes with high IRF received significantly more treatments (+1.6 [p < 0.001] and +5.3 [p = 0.002] at months 12 and 48, respectively). Patients with high SRF or PED had comparable best-corrected visual acuity outcomes but received significantly more treatments for SRF (+2.4 [p < 0.001] and +11.4 [p < 0.001] at months 12 and 48, respectively) and PED (+1.2 [p = 0.001] and +7.8 [p < 0.001] at months 12 and 48, respectively).

DISCUSSION

Patients with high macular fluid after initial treatment are at risk of losing vision that may not be compensable with higher treatment frequency for IRF. Higher treatment frequency for SRF and PED may result in comparable treatment outcomes. Quantification of macular fluid in all compartments is essential to detect eyes at risk of aggressive disease.

Deep learning in optical coherence tomography: Where are the gaps?

Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post-processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL-based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL-based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real-world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.

Imaging Histology Correlations of Intraretinal Fluid in Neovascular Age-Related Macular Degeneration

Purpose

Fluid presence and dynamism is central to the diagnosis and management of neovascular age-related macular degeneration. On optical coherence tomography (OCT), some hyporeflective spaces arise through vascular permeability (exudation) and others arise through degeneration (transudation). Herein we determined whether the histological appearance of fluid manifested this heterogeneity.

Methods

Two eyes of a White woman in her 90s with anti-vascular endothelial growth factor treated bilateral type 3 neovascularization secondary to age-related macular degeneration were osmicated, prepared for submicrometer epoxy resin sections, and correlated to eye-tracked spectral domain OCT. Examples of intraretinal tissue fluid were sought among similarly prepared donor eyes with fibrovascular scars, in a web-based age-related macular degeneration histopathology resource. Fluid stain intensity was quantified in reference to Bruch's membrane and the empty glass slide.

Results

Exudative fluid by OCT was slightly reflective and dynamically responded to anti-vascular endothelial growth factor. On histology, this fluid stained moderately, possessed a smooth and homogenous texture, and contained blood cells and fibrin. Nonexudative fluid in degenerative cysts and in outer retinal tubulation was minimally reflective on OCT and did not respond to anti-vascular endothelial growth factor. By histology, this fluid stained lightly, possessed a finely granular texture, and contained mainly tissue debris. Quantification supported the qualitative impressions of fluid stain density. Cells containing retinal pigment epithelium organelles localized to both fluid types.

Conclusions

High-resolution histology of osmicated tissue can distinguish between exudative and nonexudative fluid, some of which is transudative.

Translational Relevance

OCT and histological features of different fluid types can inform clinical decision-making and assist in the interpretation of newly available automated fluid detection algorithms.

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.

Clinical Correlation of Retinal Fluid Fluctuation Represented by Fluctuation Index in Wet Age-Related Macular Degeneration: TOWER Study Report 2

Purpose

To explore outcomes and biomarkers associated with retinal fluid instability represented by a new parameter in neovascular age-related macular degeneration (nAMD).

Methods

Patients with treatment-naïve nAMD receiving anti-vascular endothelial growth factor (VEGF) injections for a duration of 1 to 3 years were consecutively reviewed. Fluctuation Index (FI) of each eye, calculated by averaging the sum of differences in 1-mm central subfield thickness between each follow-up from months 3 to 24, was arranged into ascending order from the lowest to the highest and split equally into low, moderate, and high fluctuation groups. Outcomes were analyzed at 24 months.

Results

Of 558 eyes, FI values showed a negative correlation with a degree-response gradient with 24-month visual improvement. After controlling for baseline best-corrected visual acuity and potential confounders, eyes with low fluctuation gained more Early Treatment Diabetic Retinopathy Study letters than those in the moderate and high fluctuation group (Δ, 10.1 and 14.0 letters, respectively). Significant best-corrected visual acuity improvement from baseline to month 24 (11.8 letters) was observed exclusively in the low fluctuation group despite the indifference in the number of injections and types of anti-VEGF drug used among groups. Patients presenting with central subfield thickness of ≥405 µm or intraretinal fluid coinciding with subretinal fluid showed a significant association with foveal thickness instability during the maintenance phase.

Conclusions

Apart from the central subfield thickness values, unstable macular thickening represented by the FI was associated with some baseline features and may contribute to substandard visual outcomes.

Translational Relevance

FI may be a valuable tool for assessing therapeutic adequacy in the treatment of nAMD.

Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review

Age-related macular degeneration (AMD) is one of the leading causes of blindness in the elderly, more commonly in developed countries. Optical coherence tomography (OCT) is a non-invasive imaging device widely used for the diagnosis and management of AMD. Deep learning (DL) uses multilayered artificial neural networks (NN) for feature extraction, and is the cutting-edge technique for medical image analysis for diagnostic and prognostication purposes. Application of DL models to OCT image analysis has garnered significant interest in recent years. In this review, we aimed to summarize studies focusing on DL models used in classification and detection of AMD. Additionally, we provide a brief introduction to other DL applications in AMD, such as segmentation, prediction/prognostication, and models trained on multimodal imaging.

Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus

PURPOSE OF REVIEW

The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important role in telemedicine and has potential to shape modern healthcare ecosystems, including within ophthalmology.

RECENT FINDINGS

In this article, we examine the latest publications relevant to AI in retinal disease and discuss the currently available algorithms. We summarize four key requirements underlining the successful application of AI algorithms in real-world practice: processing massive data; practicability of an AI model in ophthalmology; policy compliance and the regulatory environment; and balancing profit and cost when developing and maintaining AI models.

SUMMARY

The Vision Academy recognizes the advantages and disadvantages of AI-based technologies and gives insightful recommendations for future directions.

Biomedical Data Annotation: An OCT Imaging Case Study

In ophthalmology, optical coherence tomography (OCT) is a widely used imaging modality, allowing visualisation of the structures of the eye with objective and quantitative cross-sectional three-dimensional (3D) volumetric scans. Due to the quantity of data generated from OCT scans and the time taken for an ophthalmologist to inspect for various disease pathology features, automated image analysis in the form of deep neural networks has seen success for the classification and segmentation of OCT layers and quantification of features. However, existing high-performance deep learning approaches rely on huge training datasets with high-quality annotations, which are challenging to obtain in many clinical applications. The collection of annotations from less experienced clinicians has the potential to alleviate time constraints from more senior clinicians, allowing faster data collection of medical image annotations; however, with less experience, there is the possibility of reduced annotation quality. In this study, we evaluate the quality of diabetic macular edema (DME) intraretinal fluid (IRF) biomarker image annotations on OCT B-scans from five clinicians with a range of experience. We also assess the effectiveness of annotating across multiple sessions following a training session led by an expert clinician. Our investigation shows a notable variance in annotation performance, with a correlation that depends on the clinician's experience with OCT image interpretation of DME, and that having multiple annotation sessions has a limited effect on the annotation quality.

Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration: a systematic review

The aim of this systematic literature review is twofold, (1) detail the impact of retinal biomarkers identifiable via optical coherence tomography (OCT) on disease progression and response to treatment in neovascular age-related macular degeneration (nAMD) and (2) establish which biomarkers are currently identifiable by artificial intelligence (AI) models and the utilisation of this technology. Following the PRISMA guidelines, PubMed was searched for peer-reviewed publications dated between January 2016 and January 2022.

POPULATION

Patients diagnosed with nAMD with OCT imaging.

SETTINGS

Comparable settings to NHS hospitals.

STUDY DESIGNS

Randomised controlled trials, prospective/retrospective cohort studies and review articles. From 228 articles, 130 were full-text reviewed, 50 were removed for falling outside the scope of this review with 10 added from the author's inventory, resulting in the inclusion of 90 articles. From 9 biomarkers identified; intraretinal fluid (IRF), subretinal fluid, pigment epithelial detachment, subretinal hyperreflective material (SHRM), retinal pigmental epithelial (RPE) atrophy, drusen, outer retinal tabulation (ORT), hyperreflective foci (HF) and retinal thickness, 5 are considered pertinent to nAMD disease progression; IRF, SHRM, drusen, ORT and HF. A number of these biomarkers can be classified using current AI models. Significant retinal biomarkers pertinent to disease activity and progression in nAMD are identifiable via OCT; IRF being the most important in terms of the significant impact on visual outcome. Incorporating AI into ophthalmology practice is a promising advancement towards automated and reproducible analyses of OCT data with the ability to diagnose disease and predict future disease conversion.

SYSTEMATIC REVIEW REGISTRATION

This review has been registered with PROSPERO (registration ID: CRD42021233200).

An Analysis of the Usage of Retinal Imaging Technology in the Detection of Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a disease that worsens the central vision of numerous individuals across the globe. Ensuring that patients are diagnosed accurately and that their symptoms are carefully monitored is essential to ensure that adequate care is delivered. To accomplish this objective, retinal imaging technology is necessary to assess the pathophysiology that is required to give an accurate diagnosis of AMD. The purpose of this review is to assess the ability of various retinal imaging technologies such as optical coherence tomography (OCT), color fundus retinal photography, fluorescein angiography, and fundus photography. The statistical methods that were conducted yielded results that suggested that using OCT in conjunction with other imaging technologies results in a higher detection of symptoms among patients that have AMD. Further investigation should be conducted to ascertain the validity of the conclusions that were stated within the review.

Diagnosis, Classification, and Assessment of the Underlying Etiology of Uveitis by Artificial Intelligence: A Systematic Review

Recent years have seen the emergence and application of artificial intelligence (AI) in diagnostic decision support systems. There are approximately 80 etiologies that can underly uveitis, some very rare, and AI may lend itself to their detection. This synthesis of the literature selected articles that focused on the use of AI in determining the diagnosis, classification, and underlying etiology of uveitis. The AI-based systems demonstrated relatively good performance, with a classification accuracy of 93-99% and a sensitivity of at least 80% for identifying the two most probable etiologies underlying uveitis. However, there were limitations to the evidence. Firstly, most data were collected retrospectively with missing data. Secondly, ophthalmic, demographic, clinical, and ancillary tests were not reliably integrated into the algorithms' dataset. Thirdly, patient numbers were small, which is problematic when aiming to discriminate rare and complex diagnoses. In conclusion, the data indicate that AI has potential as a diagnostic decision support system, but clinical applicability is not yet established. Future studies and technologies need to incorporate more comprehensive clinical data and larger patient populations. In time, these should improve AI-based diagnostic tools and help clinicians diagnose, classify, and manage patients with uveitis.

Automated Macular Fluid Volume As a Treatment Indicator for Diabetic Macular Edema

Introduction: To assess the diagnostic accuracy of automatically quantified macular fluid volume (MFV) for treatment-required diabetic macular edema (DME). Methods: This retrospective cross-sectional study included eyes with DME. The commercial software on optical coherence tomography (OCT) produced the central subfield thickness (CST), and a custom deep-learning algorithm automatically segmented the fluid cysts and quantified the MFV from the volumetric scans of an OCT angiography system. Retina specialists treated patients per standard of care based on clinical and OCT findings without access to the MFV. The main outcome measures were the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of the CST, MFV, and visual acuity (VA) for treatment indication. Results: Of 139 eyes, 39 (28%) were treated for DME during the study period and 101 (72%) were previously treated. The algorithm detected fluid in all eyes; however, only 54 eyes (39%) met the DRCR.net criteria for center-involved ME. The AUROC of MFV predicting a treatment decision of 0.81 was greater than that of CST (0.67) (P = .0048). Untreated eyes that met the optimal threshold for treatment-required DME based on MFV (>0.031 mm3) had better VA than treated eyes (P = .0053). A multivariate logistic regression model showed that MFV (P = .0008) and VA (P = .0061) were significantly associated with a treatment decision, but CST was not. Conclusions: MFV had a higher correlation with the need for treatment for DME than CST and may be especially useful for ongoing management of DME.

Therapeutic response in the HAWK and HARRIER trials using deep learning in retinal fluid volume and compartment analysis

OBJECTIVES

To assess the therapeutic response to brolucizumab and aflibercept by deep learning/OCT-based analysis of macular fluid volumes in neovascular age-related macular degeneration.

METHODS

In this post-hoc analysis of two phase III, randomised, multi-centre studies (HAWK/HARRIER), 1078 and 739 treatment-naive eyes receiving brolucizumab or aflibercept according to protocol-specified criteria in HAWK and HARRIER, respectively, were included. Macular fluid on 41,840 OCT scans was localised and quantified using a validated deep learning-based algorithm. Volumes of intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED) for all central macular areas (1, 3 and 6 mm) in nanolitres (nL) and best corrected visual acuity (BCVA) change in ETDRS letters were associated using mixed models for repeated measures.

RESULTS

Baseline IRF volumes decreased by >92% following the first intravitreal injection and consistently remained low during follow-up. Baseline SRF volumes decreased by >74% following the first injection, while PED volume resolved by 68-79% of its baseline volume. Resolution of SRF and PED was dependent on the substance and regimen used. Larger residual post-loading IRF, SRF and PED volumes were all independently associated with progressive vision loss during maintenance, where the differences in mean BCVA change between high and low fluid volume subgroups for IRF, SRF and PED were 3.4 letters (p < 0.0001), 1.7 letters (p < 0.001) and 2.5 letters (p < 0.0001), respectively.

CONCLUSIONS

Deep-learning methods allow an accurate assessment of substance and regimen efficacy. Irrespectively, all fluid compartments were found to be important markers of disease activity and were relevant for visual outcomes.

Pseudophakia as a surprising protective factor in neovascular age-related macular degeneration

PURPOSE

To assess the impact of lens status on macular function among patients treated for neovascular age-related macular degeneration (nvAMD) in whom scheduled intravitreal injections were delayed.

METHODS

We reviewed demographic and clinical data as well as macular optical coherence tomographic images of 34 patients (48 eyes) who did not follow their injection schedule during the first wave of COVID-19 in Israel. Functional worsening was defined as a loss of at least 0.1 in decimal best-corrected visual acuity (BCVA). Morphological worsening was defined as new or increased subretinal/intraretinal fluid or a new hemorrhage. OCT indices of quality were used as a measure for cataract density and progression.

RESULTS

Pseudophakia was associated with a better functional outcome than phakic status: there was a loss of 0.06±0.12 vs. 0.15±0.10 decimal BCVA in the pseudophakic and phakic eyes, respectively (P=.001). A similar trend was observed for morphological changes over the same period: there was an increase in macular thickness of 9±26% vs.12±40%, respectively (P=0.79). During the first wave of COVID-19, the index of OCT quality remained stable for phakic eyes (26±3.6 before the first wave of COVID-19, 26±2.9 afterward; P=1) and pseudophakic eyes (30±2.4 before the first wave of COVID-19, 30±2.6 afterward; P=1).

CONCLUSION

Pseudophakic eyes with nvAMD that missed their scheduled intravitreal injections experienced fewer morphological and functional complications than phakic eyes with nvAMD.

Practical guidance for imaging biomarkers in exudative age-related macular degeneration

We provide an overview of current macular imaging techniques and identify and describe biomarkers that may be of use in the routine management of macular diseases, particularly exudative age-related macular degeneration (n-AMD). This perspective includes sections on macular imaging techniques including optical coherence tomography (OCT) and OCT angiography (OCTA), classification of exudative AMD, and biomarkers in structural OCT and OCTA. Fluorescein angiography remains a vital tool for assessing the activity of neovascular lesion, while indocyanine green angiography is the preferred option for choroidal vessels imaging in neovascular AMD. OCT provides a non-invasive three-dimensional visualization of retinal architecture in vivo and is useful in the diagnosis of many imaging biomarkers of AMD-related neovascular lesions including lesion activity. OCTA is a recent advance in OCT technology that allows accurate visualization of retinal and choroidal vascular flow. OCT and OCTA have led to an updated classification of exudative AMD lesions and provide several biomarkers that help to establish a diagnosis and the disease activity status of neovascular lesions. Individualization of therapy guided by OCT and OCTA biomarkers has the potential to further improve visual outcomes in exudative AMD. Moving forwards, integration of technologically advanced imaging equipment with AI software will help ophthalmologists to provide patients with the best possible care.

Correlation of vascular and fluid-related parameters in neovascular age-related macular degeneration using deep learning

PURPOSE

To identify correlations between the vascular characteristics of macular neovascularization (MNV) obtained by optical coherence tomography angiography (OCTA) and distinct retinal fluid volumes in neovascular age-related macular degeneration (nAMD).

METHODS

In this prospective interventional study, 54 patients with treatment-naïve type 1 or 2 nAMD were included and treated with intravitreal aflibercept. At baseline and month 1, each patient underwent a SD-OCT volume scan and volumetric flow scan using a swept-source OCTA. A deep learning algorithm was used to automatically detect and quantify fluid in OCT scans. Angio Tool, a National Cancer Institute algorithm, was used to skeletonize MNV properties and quantify lesion size (LS), vessel area (VA), vessel density (VD), total number of endpoints (TNE), total number of junctions (TNJ), junction density (JD), total vessel length (TVL), average vessel length (AVL) and mean-e-lacunarity (MEL). Subsequently, linear regression models were used to investigate a correlation between OCTA parameters and fluid quantifications.

RESULTS

The median amount of fluid within the central 6-mm EDTRS ring was 173.7 nl at baseline, consisting of 156.6 nl of subretinal fluid (SRF) and 2.3 nl of intraretinal fluid (IRF). Fluid decreased significantly in all compartments to 1.76 nl (SRF) and 0.64 nl (IRF). The investigated MNV parameters did not change significantly after the first treatment. There was no significant correlation between MNV parameters and relative fluid decrease after anti-VEGF treatment. Baseline fluid correlated statistically significant but weakly with TNE (p = 0.002, R²  = 0.17), SRF with TVL (p = 0.04, R²  = 0.08), VD (p = 0.046, R²  = 0.08), TNE (p = 0.001, R²  = 0.20) and LS (p = 0.033, R²  = 0.09). IRF correlated with VA (p = 0.042, R²  = 0.08).The amount of IRF at month 1 correlated significantly but weakly with VD (p = 0.036, R²  = 0.08), JD (p = 0.019, R²  = 0.10) and MEL (p = 0.005, R²  = 0.14).

CONCLUSION

Macular neovascularization parameters at baseline and month 1 played only a minor role in the exudation process in nAMD. None of the MNV parameters were correlated with the treatment response.

Retinal fluid is associated with cytokines of aqueous humor in age-related macular degeneration using automatic 3-dimensional quantification

Background: To explain the biological role of cytokines in the eye and the possible role of cytokines in the pathogenesis of neovascular age-related macular degeneration (nAMD) by comparing the correlation between cytokine of aqueous humor concentration and optical coherence tomography (OCT) retinal fluid. Methods: Spectral-domain OCT (SD-OCT) images and aqueous humor samples were collected from 20 nAMD patient's three clinical visits. Retinal fluid volume in OCT was automatically quantified using deep learning--Deeplabv3+. Eighteen cytokines were detected in aqueous humor using the Luminex technology. OCT fluid volume measurements were correlated with changes in aqueous humor cytokine levels using Pearson's correlation coefficient (PCC). Results: The patients with intraretinal fluid (IRF) showed significantly lower levels of cytokines, such as C-X-C motif chemokine ligand 2 (CXCL2) (p = 0.03) and CXCL11 (p = 0.009), compared with the patients without IRF. And the IRF volume was negatively correlated with CXCL2 (r = -0.407, p = 0.048) and CXCL11 (r = -0.410, p = 0.046) concentration in the patients with IRF. Meanwhile, the subretinal fluid (SRF) volume was positively correlated with vascular endothelial growth factor (VEGF) concentration (r = 0.299, p = 0.027) and negatively correlated with interleukin (IL)-36β concentration (r = -0.295, p = 0.029) in the patients with SRF. Conclusion: Decreased level of VEGF was associated with decreased OCT-based retinal fluid volume in nAMD patients, while increased levels of CXCL2, CXCL11, and IL-36β were associated with decreased OCT-based retinal fluid volume in nAMD patients, which may suggest a role for inflammatory cytokines in retinal morphological changes and pathogenesis of nAMD patients.

The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review

In epidemiology, a risk factor is a variable associated with increased disease risk. Understanding the role of risk factors is significant for developing a strategy to improve global health. There is strong evidence that risk factors like smoking, alcohol consumption, previous cataract surgery, age, high-density lipoprotein (HDL) cholesterol, BMI, female gender, and focal hyper-pigmentation are independently associated with age-related macular degeneration (AMD). Currently, in the literature, statistical techniques like logistic regression, multivariable logistic regression, etc., are being used to identify AMD risk factors by employing numerical/categorical data. However, artificial intelligence (AI) techniques have not been used so far in the literature for identifying risk factors for AMD. On the other hand, artificial intelligence (AI) based tools can anticipate when a person is at risk of developing chronic diseases like cancer, dementia, asthma, etc., in providing personalized care. AI-based techniques can employ numerical/categorical and/or image data thus resulting in multimodal data analysis, which provides the need for AI-based tools to be used for risk factor analysis in ophthalmology. This review summarizes the statistical techniques used to identify various risk factors and the higher benefits that AI techniques provide for AMD-related disease prediction. Additional studies are required to review different techniques for risk factor identification for other ophthalmic diseases like glaucoma, diabetic macular edema, retinopathy of prematurity, cataract, and diabetic retinopathy.

Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions

BACKGROUND

Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review.

MATERIALS AND METHODS

We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease.

RESULTS

Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria.

CONCLUSION

In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.

Automated artificial intelligence-based system for clinical follow-up of patients with age-related macular degeneration

PURPOSE

In this study, we investigate the potential of a novel artificial intelligence-based system for autonomous follow-up of patients treated for neovascular age-related macular degeneration (AMD).

METHODS

A temporal deep learning model was trained on a data set of 84 489 optical coherence tomography scans from AMD patients to recognize disease activity, and its performance was compared with a published non-temporal model trained on the same data (Acta Ophthalmol, 2021). An autonomous follow-up system was created by augmenting the AI model with deterministic logic to suggest treatment according to the observe-and-plan regimen. To validate the AI-based system, a data set comprising clinical decisions and imaging data from 200 follow-up consultations was collected prospectively. In each case, both the autonomous AI decision and original clinical decision were compared with an expert panel consensus.

RESULTS

The temporal AI model proved superior at detecting disease activity compared with the model without temporal input (area under the curve 0.900 (95% CI 0.894-0.906) and 0.857 (95% CI 0.846-0.867) respectively). The AI-based follow-up system could make an autonomous decision in 73% of the cases, 91.8% of which were in agreement with expert consensus. This was on par with the 87.7% agreement rate between decisions made in the clinic and expert consensus (p = 0.33).

CONCLUSIONS

The proposed autonomous follow-up system was shown to be safe and compliant with expert consensus on par with clinical practice. The system could in the future ease the pressure on public ophthalmology services from an increasing number of AMD patients.

Long-term outcome of neovascular age-related macular degeneration: association between treatment outcome and major risk alleles

AIMS

To evaluate the long-term functional and anatomical outcomes of neovascular age-related macular degeneration (nvAMD) treated with intravitreal anti-vascular endothelial growth factor (anti-VEGF) for up to 10 years, and to identify associated risk factors.

METHODS

Clinical and optical coherence tomography findings were retrieved for nvAMD cases treated with intravitreal anti-VEGF compounds using a treat-and-extend protocol. In addition, the major risk alleles for AMD in the CFH (rs1061170), HTRA1 (rs1200638) and C3 (rs2230199) genes were genotyped.

RESULTS

From 276 eligible eyes in 206 patients, 80 eyes (29%) in 66 patients (32.0%) had a follow-up period of ≥8 years and were included in this study. Over a 10-year period, 73.3±28.0 (mean±SD) anti-VEGF injections were administered. Best-corrected visual acuity (BCVA; LogMAR) deteriorated from 0.55±0.53 at baseline to 1.00±0.73 at 10 years (p<0.0005). Central subfield thickness (CST) decreased from 415.8±162.1 µm at baseline to 323±113.6 µm (p<0.0005) after three monthly injections and remained lower than baseline throughout the follow-up period. Visual outcome was associated with BCVA and intraretinal fluid (IRF) at baseline, macular atrophy, and macular thinning at follow-up. The decrease in CST was inversely correlated with the number of CFH and/or C3 risk alleles carried by the patient (Pearson's r: -0.608; p=0.003).

CONCLUSIONS

Patients with nvAMD who received anti-VEGF therapy for 10 years developed substantial vision loss associated with the presence of IRF at baseline and macular atrophy. Major risk alleles for AMD in two complement genes were associated with a reduced long-term reduction in macular thickness.

Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives

Background

The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic.

Main text

At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns.

Conclusions

This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.

Deep learning to infer visual acuity from optical coherence tomography in diabetic macular edema

Purpose

Diabetic macular edema (DME) is one of the leading causes of visual impairment in diabetic retinopathy (DR). Physicians rely on optical coherence tomography (OCT) and baseline visual acuity (VA) to tailor therapeutic regimen. However, best-corrected visual acuity (BCVA) from chart-based examinations may not wholly reflect DME status. Chart-based examinations are subjected findings dependent on the patient’s recognition functions and are often confounded by concurrent corneal, lens, retinal, optic nerve, or extraocular disorders. The ability to infer VA from objective optical coherence tomography (OCT) images provides the predicted VA from objective macular structures directly and a better understanding of diabetic macular health. Deviations from chart-based and artificial intelligence (AI) image-based VA will prompt physicians to assess other ocular abnormalities affecting the patients VA and whether pursuing anti-VEGF treatment will likely yield increment in VA.

Materials and methods

We enrolled a retrospective cohort of 251 DME patients from Big Data Center (BDC) of Taipei Veteran General Hospital (TVGH) from February 2011 and August 2019. A total of 3,920 OCT images, labeled as “visually impaired” or “adequate” according to baseline VA, were grouped into training (2,826), validation (779), and testing cohort (315). We applied confusion matrix and receiver operating characteristic (ROC) curve to evaluate the performance.

Results

We developed an OCT-based convolutional neuronal network (CNN) model that could classify two VA classes by the threshold of 0.50 (decimal notation) with an accuracy of 75.9%, a sensitivity of 78.9%, and an area under the ROC curve of 80.1% on the testing cohort.

Conclusion

This study demonstrated the feasibility of inferring VA from routine objective retinal images.

Translational relevance

Serves as a pilot study to encourage further use of deep learning in deriving functional outcomes and secondary surrogate endpoints for retinal diseases.

The Impact of Early Residual Fluid After Anti-Vascular Endothelial Growth Factor Initiation in Patients With Neovascular Age-Related Macular Degeneration: A Meta-Analysis Review

Fluid in neovascular age-related macular degeneration is often used to assess patient response to anti-vascular endothelial growth factor therapy. Various studies theorize that early residual fluid (ERF), noted as persistence of intraretinal fluid and subretinal fluid after the anti-vascular endothelial growth factor loading phase (LP), may be predictive of visual outcomes. This meta-analysis examined the existing literature on the relationship between ERF and long-term visual acuity (VA) and found that those who were fluid-free after the LP tended to have the highest VA gains overall. Early intraretinal fluid appeared to be associated with reduced VA gains, whereas the impact of early sub-retinal fluid was more debated. For those with ERF, monthly or more frequent dosing regimens following the LP appeared most optimal for VA. As most studies in this review were post hoc analyses, this highlights the need for real-world studies investigating ERF and its effect on visual outcomes in neovascular age-related macular degeneration. [Ophthalmic Surg Lasers Imaging Retina 2022;53:506-513.].

Deep learning: applications in retinal and optic nerve diseases

Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large amount of annotated data and allowing the software to develop its own set of rules (i.e. learn) by adjusting the parameters inside the model (network) during a training process in order to complete the task on its own. One major limitation of traditional programming is that, with complex tasks, it may require an extensive set of rules to accurately complete the assignment. Additionally, traditional programming can be susceptible to human bias from programmer experience. With the dramatic increase in the amount and the complexity of clinical data, DL has been utilised to automate data analysis and thus to assist clinicians in patient management. This review will present the latest advances in DL, for managing posterior eye diseases as well as DL-based solutions for patients with vision loss.

Clinical Effect of Tongmai Fuming Decoction on Neovascular Ophthalmopathy

Background

The incidence of neovascular eye disease is increasing year by year, seriously threatening human vision health and becoming an urgent public health problem. Tongmai fuming decoction as an experienced prescription can treat ischemic eye disease.

Objective

To investigate the therapeutic effect of Tongmai fuming decoction combined with anti-VEGF therapy on neovascular ophthalmopathy.

Methods

52 patients (62 eyes) with neovascular ophthalmopathy who met the inclusion criteria from January 2018 to July 2020 were randomly divided into the control and observation groups. The control group was given an intravitreal injection of antivascular endothelial growth factor (VEGF) drugs once a day combined with on-demand treatment. The observation group was treated with traditional Chinese medicine Tongmai fuming decoction in addition to the treatment of anti-VEGF drugs. The best-corrected visual acuity (BCVA) was examined before and after treatment, and optical coherence tomography angiography (OCTA) was used to examine the mean retinal thickness and neovascularization in the macular area. Patients were followed for one year and the number of anti-VEGF injections was recorded.

Results

After treatment, the average thickness of BCVA and macular retina in the two groups significantly improved. The BCVA of the control group was 0.59 ± 0.39 3 months after treatment, and that of the experimental group was 0.42 ± 0.25 3 months after treatment. The average thickness of the macular retina in the control group was 304.8 ± 79.7 3 months after treatment, and that in the experimental group was 267.7 ± 64.6 3 months after treatment; The average number of injections of anti-VEGF therapy in the control group was 2.32 ± 1.15 times, and that in the experimental group was 1.74 ± 0.76 times. There was a significant difference between the two groups.

Conclusion

Tongmai fuming decoction and anti-VEGF therapy have a synergistic effect in the treatment of neovascular ophthalmopathy, which can reduce the treatment times of anti-VEGF drugs.

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

This systematic review explores the use of artificial intelligence (AI) in the analysis of biofluid markers in age-related macular degeneration (AMD). We detail the accuracy and validity of AI in diagnostic and prognostic models and biofluid markers that provide insight into AMD pathogenesis and progression. This review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A comprehensive search was conducted across 5 electronic databases including Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, Medline, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics in AMD were included. Identified studies were assessed for risk of bias and critically appraised using the Joanna Briggs Institute Critical Appraisal tools. A total of 10,264 articles were retrieved from all databases and 37 studies met the inclusion criteria, including 15 cross-sectional studies, 15 prospective cohort studies, five retrospective cohort studies, one randomized controlled trial, and one case–control study. The majority of studies had a general focus on AMD (58%), while neovascular AMD (nAMD) was the focus in 11 studies (30%), and geographic atrophy (GA) was highlighted by three studies. Fifteen studies examined disease characteristics, 15 studied risk factors, and seven guided treatment decisions. Altered lipid metabolism (HDL-cholesterol, total serum triglycerides), inflammation (c-reactive protein), oxidative stress, and protein digestion were implicated in AMD development and progression. AI tools were able to both accurately differentiate controls and AMD patients with accuracies as high as 87% and predict responsiveness to anti-VEGF therapy in nAMD patients. Use of AI models such as discriminant analysis could inform prognostic and diagnostic decision-making in a clinical setting. The identified pathways provide opportunity for future studies of AMD development and could be valuable in the advancement of novel treatments.

Prospective, Longitudinal Study: Daily Self-Imaging with Home OCT for Neovascular Age-Related Macular Degeneration

OBJECTIVE

To validate the performance of the Notal Vision Home OCT (NVHO) system for daily self-imaging at home and characterize the retinal fluid dynamics of patients with neovascular age-related macular degeneration (nAMD).

DESIGN

Prospective observational study.

SUBJECTS

Fifteen participants who had at least 1 eye with nAMD and underwent anti-VEGF treatments.

METHODS

The participants performed daily self-imaging at home using NVHO for 3 months. The scans were uploaded to the cloud, analyzed using the Notal OCT Analyzer (NOA), evaluated by human experts for fluid presence, and compared with in-office OCT scans.

MAIN OUTCOME MEASURES

Weekly self-scan rate, image quality, scan duration, agreement between NOA and human expert grading for fluid presence, agreement between NVHO and in-office OCT scans for fluid presence, central subfield thickness (CST) and retinal fluid volume, and the characteristics of fluid dynamics during the study and in response to treatments.

RESULTS

The mean weekly scan frequency was 5.7 ± 0.9 scans per week, and 93% of the scans were eligible for NOA analyses. The median scan time was 42 seconds. The NOA and human experts agreed on the fluid status in 83% of the scans, and discrepancies were limited to trace amounts of fluid. The NVHO scans analyzed using NOA and the in-office OCT scans graded by human experts agreed on the fluid status in 96% of the cases. The CST and retinal fluid volume measurements using the home OCT and in-office OCT scans demonstrated a Pearson correlation coefficient of r = 0.90 and r = 0.92, respectively. Novel parameters, such as retinal fluid volume and area under the curve (AUC) of retinal fluid volume, demonstrated wide variations in fluid exudation and fluid load over time among the patients. Parameters such as the rate of reduction in fluid volume in the first week after treatment and AUC between treatments captured the speed and duration of the response to anti-VEGF agents.

CONCLUSIONS

Daily home OCT imaging is feasible among patients with nAMD. It demonstrated good agreement with human expert grading for retinal fluid identification and excellent agreement with the in-clinic OCT scans. Home OCT allows for detailed graphical and mathematical analyses of retinal fluid volume trajectories, including novel parameters to inform clinical decision making.

Association Between Visual Acuity and Fluid Compartments with Treat-and-Extend Intravitreal Aflibercept in Neovascular Age-Related Macular Degeneration: An ARIES Post Hoc Analysis

INTRODUCTION

Recently, there has been growing interest in exploring the relationship between visual acuity and fluid localization in different retinal compartments. This post hoc analysis of the ARIES study explores the relationship between the presence of intraretinal fluid (IRF) and subretinal fluid (SRF), both at baseline and throughout treatment, and best-corrected visual acuity (BCVA) in patients with neovascular age-related macular degeneration (nAMD) treated with intravitreal aflibercept (IVT-AFL) in a treat-and-extend regimen.

METHODS

ARIES (NCT02581891) was a multicenter, randomized, phase 3b/4 study comparing the efficacy of two IVT-AFL treat-and-extend regimens over 2 years in patients with treatment-naïve nAMD. This post hoc analysis explores the relationship between the presence of SRF/IRF and absolute BCVA (letter score) at baseline and fixed visits.

RESULTS

In 210 patients (treat-and-extend treatment arms combined), SRF presence at baseline was associated at every time point with a numerically higher mean BCVA than if absent, with 10 more letters at week 104. IRF presence at baseline was associated at all but one time point with a numerically lower mean BCVA than if absent (week 104, 8-letter difference). Baseline SRF+IRF was associated with lower BCVA (week 104, 7-letter difference) than if only SRF was present, but higher BCVA (week 104, 8-letter difference) than if only IRF was present. Absence of SRF+IRF was not associated with better BCVA at any time point during the study.

CONCLUSION

In ARIES, in patients with nAMD treated with IVT-AFL, the presence of SRF was associated with better visual acuity, whereas IRF was associated with poorer visual acuity. The findings of this post hoc analysis suggest that differentiating IRF from SRF may offer better prognostic value in guiding treatment-extension decisions than the use of combined or "any" IRF and SRF. Prospective trials are needed to validate these results and determine their clinical relevance. TRIAL REGISTRATION NUMBER (CLINICALTRIALS.GOV): NCT02581891. Association between Visual Acuity and Fluid Compartments with Treat-and-Extend Intravitreal Aflibercept in Neovascular Age-Related Macular Degeneration: An ARIES Post Hoc Analysis: A Video Abstract (MP4 308264 KB).

Impact of Persistent Retinal Fluid in Patients with Neovascular Age-Related Macular Degeneration in Routine Clinical Practice

BACKGROUND AND OBJECTIVE

To determine whether quantification of intraretinal fluid (IRF) and subretinal fluid (SRF) can be used as a biomarker for predicting visual prognosis in routine clinical practice.

PATIENTS AND METHODS

Retrospective, nonrandomized cohort study review of patients with neovascular age-related macular degeneration from January 1, 2012, to March 1, 2018.

RESULTS

In the 286-patient cohort, the mean baseline, 6-month, and 12-month best-corrected visual acuity (BCVA) was 60.24 ± 18.63, 65.57 ± 16.56, and 65.61 ± 17.37 Early Treatment Diabetic Retinopathy Study (ETDRS) letters, respectively (P < .001). The regression coefficient in the linear mixed effects regression model quantifying the association between eyes in the fourth and first quartile of IRF and 12-month BCVA was -4.14 (95% CI, -6.65 to -1.63) (P = .001) ETDRS letters. The regression coefficient quantifying the association between eyes in the fourth and first quartile of SRF and 12-month BCVA was -0.7 (95% CI, -3.07 to 1.27) (P = .56) ETDRS letters.

CONCLUSION

IRF and SRF are valuable biomarkers for BCVA outcomes in treatment-naïve neovascular age-related macular degeneration in routine clinical practice. [Ophthalmic Surg Lasers Imaging 2022;53:317-324.].