Prediction of Post-Treatment Visual Acuity in Age-Related Macular Degeneration Patients With an Interpretable Machine Learning Method.

Purpose: We evaluated the features predicting visual acuity (VA) after one year in neovascular age-related macular degeneration (nAMD) patients. Methods: A total of 527 eyes of 506 patients were included. Machine learning (ML) models were trained to predict VA deterioration beyond a logarithm of the minimum angle of resolution of 1.0 after 1 year based on the sequential addition of multimodal data. BaseM models used clinical data (age, sex, treatment regimen, and VA), SegM models included fluid volumes from optical coherence tomography (OCT) images, and RawM models used probabilities of visual deterioration (hereafter probability) from deep learning classifiers trained on baseline OCT (OCT0) and OCT after three loading doses (OCT3), fluorescein angiography, and indocyanine green angiography. We applied SHapley Additive exPlanations (SHAP) for machine learning model interpretation. Results: The RawM model based on the probability of OCT0 outperformed the SegM model (area under the receiver operating characteristic curve of 0.95 vs. 0.91). Adding probabilities from OCT3, fluorescein angiography, and indocyanine green angiography to RawM showed minimal performance improvement, highlighting the practicality of using raw OCT0 data for predicting visual outcomes. Applied SHapley Additive exPlanations analysis identified VA after 3 months and OCT3 probability values as the most influential features over quantified fluid segments. Conclusions: Integrating multimodal data to create a visual predictive model yielded accurate, interpretable predictions. This approach allowed the identification of crucial factors for predicting VA in patients with nAMD. Translational Relevance: Interpreting a predictive model for 1-year VA in patients with nAMD from multimodal data allowed us to identify crucial factors for predicting VA.

AMD-SD: An Optical Coherence Tomography Image Dataset for wet AMD Lesions Segmentation.

Wet Age-related Macular Degeneration (wet AMD) is a common ophthalmic disease that significantly impacts patients' vision. Optical coherence tomography (OCT) examination has been widely utilized for diagnosing, treating, and monitoring wet AMD due to its cost-effectiveness, non-invasiveness, and repeatability, positioning it as the most valuable tool for diagnosis and tracking. OCT can provide clear visualization of retinal layers and precise segmentation of lesion areas, facilitating the identification and quantitative analysis of abnormalities. However, the lack of high-quality datasets for assessing wet AMD has impeded the advancement of related algorithms. To address this issue, we have curated a comprehensive wet AMD OCT Segmentation Dataset (AMD-SD), comprising 3049 B-scan images from 138 patients, each annotated with five segmentation labels: subretinal fluid, intraretinal fluid, ellipsoid zone continuity, subretinal hyperreflective material, and pigment epithelial detachment. This dataset presents a valuable opportunity to investigate the accuracy and reliability of various segmentation algorithms for wet AMD, offering essential data support for developing AI-assisted clinical applications targeting wet AMD.

Linking disease activity with optical coherence tomography angiography in neovascular age related macular degeneration using artificial intelligence.

To investigate quantitative associations between AI-assessed disease activity and optical coherence tomography angiography (OCTA)-derived parameters in patients with neovascular age-related macular degeneration (nAMD) undergoing anti-VEGF therapy. OCTA and SD-OCT images obtained from multicenter, randomized study data were evaluated. A deep learning algorithm (RetInSight) was used to detect and quantify macular fluid on SD-OCT. Mixed effects models were applied to evaluate correlations between fluid volumes, macular neovascularization (MNV)-type and OCTA-derived MNV parameters; lesion size (LS) and vessel area (NVA). 230 patients were included. A significant positive correlation was observed between SRF and NVA (estimate = 199.8 nl/mm2, p = 0.023), while a non-significant but negative correlation was found between SRF and LS (estimate = - 71.3 nl/mm2, p = 0.126). The presence of Type I and Type II MNV was associated with significantly less intraretinal fluid (IRF) compared to Type III MNV (estimate type I:- 52.1 nl, p = 0.019; estimate type II:- 51.7 nl, p = 0.021). A significant correlation was observed between pigment epithelial detachment (PED) and the interaction between NVA and LS (estimate:28.97 nl/mm2; p = 0.012). Residual IRF at week 12 significantly correlated to baseline NVA (estimate:38.1 nl/mm2; p = 0.015) and LS (estimate:- 22.6 nl/mm2; p = 0.012). Fluid in different compartments demonstrated disparate associations with MNV OCTA features. While IRF at baseline was most pronounced in type III MNV, residual IRF was driven by neovascular MNV characteristics. Greater NVA in proportion to LS was associated with higher amounts of SRF and PED. The correlation between these parameters may represent MNV maturation and can be used as a biomarker for resolution of disease activity. AI-based OCT analysis allows for a deeper understanding of neovascular disease in AMD and the potential to adjust therapeutic strategies to optimize outcomes through precision medicine.

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 (ß = 0.377, P < 0.001), avascular SHRM (ß = 0.306, P = 0.032), and ELM impairment area (ß = 0.301, P = 0.036) in multivariate model. EZ and ELM impairment were primarily associated with baseline avascular SHRM (ß = 0.374, p = 0.003; ß = 0.388, P < 0.001, respectively), while ONL impairment primarily associated with MNV (ß = 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.

Study protocol: optical coherence tomography angiography for the detection of neovascular age-related macular degeneration: a comprehensive multicentre diagnostic accuracy study in the UK-the ATHENA study.

INTRODUCTION: The diagnosis of neovascular age-related macular degeneration (nAMD), the leading cause of visual impairment in the developed world, relies on the interpretation of various imaging tests of the retina. These include invasive angiographic methods, such as Fundus Fluorescein Angiography (FFA) and, on occasion, Indocyanine-Green Angiography (ICGA). Newer, non-invasive imaging modalities, predominately Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA), have drastically transformed the diagnostic approach to nAMD. The aim of this study is to undertake a comprehensive diagnostic accuracy assessment of the various imaging modalities used in clinical practice for the diagnosis of nAMD (OCT, OCTA, FFA and, when a variant of nAMD called Polypoidal Choroidal Vasculopathy is suspected, ICGA) both alone and in various combinations. METHODS AND ANALYSIS: This is a non-inferiority, prospective, randomised diagnostic accuracy study of 1067 participants. Participants are patients with clinical features consistent with nAMD who present to a National Health Service secondary care ophthalmology unit in the UK. Patients will undergo OCT as per standard practice and those with suspicious features of nAMD on OCT will be approached for participation in the study. Patients who agree to take part will also undergo both OCTA and FFA (and ICGA if indicated). Interpretation of the imaging tests will be undertaken by clinicians at recruitment sites. A randomised design was selected to avoid bias from consecutive review of all imaging tests by the same clinician. The primary outcome of the study will be the difference in sensitivity and specificity between OCT+OCTA and OCT+FFA (±ICGA) for nAMD detection as interpreted by clinicians at recruitment sites. ETHICS AND DISSEMINATION: The study has been approved by the South Central-Oxford B Research Ethics Committee with reference number 21/SC/0412.Dissemination of study results will involve peer-review publications, presentations at major national and international scientific conferences. TRIAL REGISTRATION NUMBER: ISRCTN18313457.

Prediction of neovascular age-related macular degeneration recurrence using optical coherence tomography images with a deep neural network.

Neovascular age-related macular degeneration (nAMD) can result in blindness if left untreated, and patients often require repeated anti-vascular endothelial growth factor injections. Although, the treat-and-extend method is becoming popular to reduce vision loss attributed to recurrence, it may pose a risk of overtreatment. This study aimed to develop a deep learning model based on DenseNet201 to predict nAMD recurrence within 3 months after confirming dry-up 1 month following three loading injections in treatment-naïve patients. A dataset of 1076 spectral domain optical coherence tomography (OCT) images from 269 patients diagnosed with nAMD was used. The performance of the model was compared with that of 6 ophthalmologists, using 100 randomly selected samples. The DenseNet201-based model achieved 53.0% accuracy in predicting nAMD recurrence using a single pre-injection image and 60.2% accuracy after viewing all the images immediately after the 1st, 2nd, and 3rd injections. The model outperformed experienced ophthalmologists, with an average accuracy of 52.17% using a single pre-injection image and 53.3% after examining four images before and after three loading injections. In conclusion, the artificial intelligence model demonstrated a promising ability to predict nAMD recurrence using OCT images and outperformed experienced ophthalmologists. These findings suggest that deep learning models can assist in nAMD recurrence prediction, thus improving patient outcomes and optimizing treatment strategies.

Texture-Based Radiomic SD-OCT Features Associated With Response to Anti-VEGF Therapy in a Phase III Neovascular AMD Clinical Trial.

Purpose: The goal of this study was to evaluate the role of texture-based baseline radiomic features (Fr) and dynamic radiomics alterations (delta, FΔr) within multiple targeted compartments on optical coherence tomography (OCT) scans to predict response to anti-vascular endothelial growth factor (VEGF) therapy in neovascular age-related macular degeneration (nAMD). Methods: HAWK is a phase 3 clinical trial data set of active nAMD patients (N = 1082) comparing brolucizumab and aflibercept. This analysis included patients receiving 6 mg brolucizumab or 2 mg aflibercept and categorized as complete responders (n = 280) and incomplete responders (n = 239) based on whether or not the eyes achieved/maintained fluid resolution on OCT. A total of 481 Fr were extracted from each of the fluid, subretinal hyperreflective material (SHRM), retinal tissue, and sub-retinal pigment epithelium (RPE) compartments. Most discriminating eight baseline features, selected by the minimum redundancy, maximum relevance feature selection, were evaluated using a quadratic discriminant analysis (QDA) classifier on the training set (Str, n = 363) to differentiate between the two patient groups. Classifier performance was subsequently validated on independent test set (St, n = 156). Results: In total, 519 participants were included in this analysis from the HAWK phase 3 study. There were 280 complete responders and 219 incomplete responders. Compartmental analysis of radiomics featured identified the sub-RPE and SHRM compartments as the most distinguishing between the two response groups. The QDA classifier yielded areas under the curve of 0.78, 0.79, and 0.84, respectively, using Fr, FΔr, and combined Fr, FΔr, and Fc on St. Conclusions: Utilizing compartmental static and dynamic radiomics features, unique differences were identified between eyes that respond differently to anti-VEGF therapy in a large phase 3 trial that may provide important predictive value. Translational Relevance: Imaging biomarkers, such as radiomics features identified in this analysis, for predicting treatment response are needed to enhanced precision medicine in the management of nAMD.

Deep-learning based automated quantification of critical optical coherence tomography features in neovascular age-related macular degeneration.

PURPOSE: To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD). METHODS: In this IRB-approved study, optical coherence tomography (OCT) data from 50 patients (50 eyes) with exudative nAMD were retrospectively analysed. Two models, A1 and A2, were created based on gradings from two masked readers, R1 and R2. Area under the curve (AUC) values gauged detection performance, and quantification between readers and models was evaluated using Dice and correlation (R2) coefficients. RESULTS: The deep learning-based algorithms had high accuracies for all fluid types between all models and readers: per B-scan IRF AUCs were 0.953, 0.932, 0.990, 0.942 for comparisons A1-R1, A1-R2, A2-R1 and A2-R2, respectively; SRF AUCs were 0.984, 0.974, 0.987, 0.979; and nPED AUCs were 0.963, 0.969, 0.961 and 0.966. Similarly, the R2 coefficients for IRF were 0.973, 0.974, 0.889 and 0.973; SRF were 0.928, 0.964, 0.965 and 0.998; and nPED were 0.908, 0.952, 0.839 and 0.905. The Dice coefficients for IRF averaged 0.702, 0.667, 0.649 and 0.631; for SRF were 0.699, 0.651, 0.692 and 0.701; and for nPED were 0.636, 0.703, 0.719 and 0.775. In an inter-observer comparison between manual readers R1 and R2, the R2 coefficient was 0.968 for IRF, 0.960 for SRF, and 0.906 for nPED, with Dice coefficients of 0.692, 0.660 and 0.784 for the same features. CONCLUSIONS: Our deep learning-based method applied on nAMD can segment critical OCT features with performance akin to manual grading.

Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography.

Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-related macular degeneration (AMD). The development of MA in patients with AMD results in irreversible blindness, and there is currently no effective method of early diagnosis of this condition, despite the recent development of unique treatments. Using OCT dataset of a total of 2211 B-scans from 45 volumetric scans of 8 patients, a convolutional neural network using one-against-all strategy was trained to present all six atrophic features followed by a validation to evaluate the performance of the models. The model predictive performance has achieved a mean dice similarity coefficient score of 0.706 ± 0.039, a mean Precision score of 0.834 ± 0.048, and a mean Sensitivity score of 0.615 ± 0.051. These results show the unique potential of using artificially intelligence-aided methods for early detection and identification of the progression of MA in wet AMD, which can further support and assist clinical decisions.

Efficacy of Aflibercept as initial treatment for neovascular age-related macular degeneration in an Iraqi patient sample.

Age-related macular degeneration (AMD) is a progressive degenerative eye disorder that primarily affects individuals over 50. It causes gradual loss of central vision and can lead to irreversible severe visual loss if left untreated. AMD is a leading cause of blindness in the developed world. This study aimed to investigate the effects of a loading dosage of intravitreal Aflibercept on functional and morphological responses in neovascular AMD, considering demographic characteristics and the link between AMD-related retinal symptoms at presentations. A prospective interventional study was conducted from November 2021 to September 2022 on a sample of Iraqi patients with neovascular AMD who had active choroidal neovascularization (CNV) lesions confirmed by OCT-A and received intravitreal Aflibercept 2mg injection as initial therapy (3 loading doses). Best-corrected visual acuity (BCVA) was used to measure functional responses, and central macular thickness (CMT) and maximum area of the retinal thickness (MART) (by SD-OCT) were used to measure morphological responses. The study included 48 patients (57 eyes) with active neovascular AMD. The mean difference of BCVA in log MAR (0.2 ± 0.7) significantly improved from 1.3±0.7 at baseline to 1.1±0.8 after loading Aflibercept (P=0.034). The mean difference in CMT 113.6 ± 125.9 was statistically significant (P<0.0001). Also, the mean change in MART significantly decreased from 444.2 ± 127.1 µm at baseline to 348.7±74.5 µm (p < 0.0001) after loading Aflibercept. This study demonstrated that Aflibercept is a functionally and anatomically successful treatment for neovascular AMD.

Prediction of treatment outcome in neovascular age-related macular degeneration using a novel convolutional neural network.

While prognosis and risk of progression are crucial in developing precise therapeutic strategy in neovascular age-related macular degeneration (nAMD), limited predictive tools are available. We proposed a novel deep convolutional neural network that enables feature extraction through image and non-image data integration to seize imperative information and achieve highly accurate outcome prediction. The Heterogeneous Data Fusion Net (HDF-Net) was designed to predict visual acuity (VA) outcome (improvement ≥ 2 line or not) at 12th months after anti-VEGF treatment. A set of pre-treatment optical coherence tomography (OCT) image and non-image demographic features were employed as input data and the corresponding 12th-month post-treatment VA as the target data to train, validate, and test the HDF-Net. This newly designed HDF-Net demonstrated an AUC of 0.989 (95% CI 0.970-0.999), accuracy of 0.936 [95% confidence interval (CI) 0.889-0.964], sensitivity of 0.933 (95% CI 0.841-0.974), and specificity of 0.938 (95% CI 0.877-0.969). By simulating the clinical decision process with mixed pre-treatment information from raw OCT images and numeric data, HDF-Net demonstrated promising performance in predicting individualized treatment outcome. The results highlight the potential of deep learning to simultaneously process a broad range of clinical data to weigh and leverage the complete information of the patient. This novel approach is an important step toward real-world personalized therapeutic strategy for typical nAMD.

DEVELOPMENT AND VALIDATION OF AN EXPLAINABLE ARTIFICIAL INTELLIGENCE FRAMEWORK FOR MACULAR DISEASE DIAGNOSIS BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

PURPOSE: To develop and validate an artificial intelligence framework for identifying multiple retinal lesions at image level and performing an explainable macular disease diagnosis at eye level in optical coherence tomography images. METHODS: A total of 26,815 optical coherence tomography images were collected from 865 eyes, and 9 retinal lesions and 3 macular diseases were labeled by ophthalmologists, including diabetic macular edema and dry/wet age-related macular degeneration. We applied deep learning to classify retinal lesions at image level and random forests to achieve an explainable disease diagnosis at eye level. The performance of the integrated two-stage framework was evaluated and compared with human experts. RESULTS: On testing data set of 2,480 optical coherence tomography images from 80 eyes, the deep learning model achieved an average area under curve of 0.978 (95% confidence interval, 0.971-0.983) for lesion classification. In addition, random forests performed accurate disease diagnosis with a 0% error rate, which achieved the same accuracy as one of the human experts and was better than the other three experts. It also revealed that the detection of specific lesions in the center of macular region had more contribution to macular disease diagnosis. CONCLUSION: The integrated method achieved high accuracy and interpretability in retinal lesion classification and macular disease diagnosis in optical coherence tomography images and could have the potential to facilitate the clinical diagnosis.

EFFECT OF RETINAL THICKNESS VARIABILITY ON VISUAL OUTCOMES AND FLUID PERSISTENCE IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION: A Post Hoc Analysis of the HAWK and HARRIER Studies.

PURPOSE: To determine the association between central subfield thickness (CST) variability and visual outcomes in eyes with neovascular age-related macular degeneration treated with anti-vascular endothelial growth factor therapies. METHODS: In this post hoc, treatment-agnostic analysis, patients (N = 1,752) were grouped into quartiles of increasing CST variation. The association between CST variability and best-corrected visual acuity was measured from baseline, or from the end of the loading phase, until the end of the study using a multilevel modeling for repeated-measures model. The association between CST variability and the presence of retinal fluid was also assessed. RESULTS: Increased CST variability was associated with worse best-corrected visual acuity outcomes at the end of study, with a least-square mean difference in best-corrected visual acuity of 8.9 Early Treatment Diabetic Retinopathy Study letters between the quartiles with the lowest and highest CST variability at the final visit. Increased variability was also associated with a higher mean fraction of visits with the presence of fluid. CONCLUSION: More stable CST was associated with better visual outcomes at the end of treatment suggesting that CST variability may provide a more reliable prognostic marker of visual outcomes than the presence of fluid alone, with the potential to enhance the clinical care of neovascular age-related macular degeneration patients.

Intraretinal, sub-retinal, and sub-retinal pigmented epithelium fluid in non-exudative age-related macular degeneration: follow-up with OCT imaging.

BACKGROUND/OBJECTIVES: To evaluate the presence and evolution of fluid in non-exudative age-related macular degeneration (AMD) through serial OCT. SUBJECTS/METHODS: A retrospective analysis of eyes with non-exudative AMD with a minimum of 4 year follow-up was done. Parameters including intraretinal fluid (IRF), subretinal fluid (SRF), and sub-retinal pigment epithelium (RPE) fluid (SRPEF); subfoveal choroidal thickness (SFCT) and type of drusen were evaluated using optical coherence tomography (OCT) scans at baseline and follow up visits. RESULTS: Seventy-two eyes (in 63 patients) were followed up for an average of 5.83 ± 2.17 years. A total of 26/72 (36%) and 29/65 (52%) of the non-exudative eyes had fluid during baseline and the last visit. Seven eyes (10%) out of 72 eyes converted into exudative AMD or neo-vascular AMD (nAMD) during the study period. SRPEF at baseline was most common fluid location for non-exudative eyes that eventually converted to nAMD. CONCLUSION: Non-exudative fluid including IRF, SRF, and SRPEF is seen in patients with non-exudative AMD with increasing incidence during long term follow-up.

CHANGES IN SYSTEMIC LEVELS OF VASCULAR ENDOTHELIAL GROWTH FACTOR AFTER INTRAVITREAL INJECTION OF AFLIBERCEPT OR BROLUCIZUMAB FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION.

PURPOSE: To analyze and compare the effects of intravitreal brolucizumab versus aflibercept on systemic vascular endothelial growth factor (VEGF)-A levels in patients with neovascular age-related macular degeneration. METHODS: In this prospective interventional case series study, brolucizumab (6.0 mg/50 µL) or aflibercept (2.0 mg/50 µL) was injected intravitreally in 30 patients each. Blood samples were drawn at baseline and 7 days and 28 days after the first injection. Systemic VEGF-A levels were measured using enzyme-linked immunosorbent assay. Thirty healthy individuals served as controls. RESULTS: The median baseline systemic VEGF-A levels in the brolucizumab, aflibercept, and control groups were 10.8 (8.0-13.2), 12.0 (8.0-18.5), and 10.0 (8.0-15.1) pg/mL, respectively (P = 0.315). In the brolucizumab group, VEGF-A levels significantly decreased to 8.0 (8.0-11.5) pg/mL on Day 7 (P = 0.0254) and to 8.0 (8.0-8.0) pg/mL on Day 28 (P < 0.001). In the aflibercept group, VEGF-A levels significantly decreased to 8.0 (8.0-8.0) pg/mL on Day 7 (P < 0.001) but returned to the baseline level, 12.5 (8.5-14.6) pg/mL, on Day 28 (P = 0.120). Vascular endothelial growth factor-A levels were significantly different between the treatment groups after 28 days (P < 0.001). CONCLUSION: Intravitreal brolucizumab resulted in a sustained reduction of systemic VEGF-A levels until 28 days posttreatment, which raises concerns regarding its safety and long-term effects.

Polypoidal Choroidal Vasculopathy Diagnosis and Neovascular Activity Evaluation Using Optical Coherence Tomography Angiography.

PURPOSE: To examine choroidal neovascularization (CNV) characteristics in patients with exudative age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV), using swept-source optical coherence tomography angiography (SS-OCTA), and investigate agreement with OCT B-scan, fundus fluorescein angiography (FFA), and indocyanine green angiography (ICGA) by two different examiners. METHODS: This is a retrospective multicentric study that involved patients with a history of AMD and PCV. Examiner A, who had access to OCTA, B-scan OCT, FFA, and ICGA imaging, had to differentiate between AMD and PCV, study the activity of AMD using Coscas' criteria (active vs. quiescent), and categorize PCV subtypes, while examiner B had only access to OCTA. Then, the diagnostic concordance was assessed between both examiners. RESULTS: A total of 27 patients (11 females (40.7%) and 16 males (59.3%), P = 0.231) were included in the analysis. Among those, 13 patients presented with neovascular AMD and 14 patients with PCV. There were 92.3% of correct answers regarding appropriate diagnosis and lesion characterization among AMD patients, against 61.5% of correct answers among PCV patients. The overall interrater reliability agreement between examiners, using Cohen's kappa coefficient (κ) was 0.70 (0.5082-0.8916). Disagreement was found with one active AMD misdiagnosed as inactive AMD, three inactive PCV misdiagnosed as inactive AMD, and one inactive PCV misdiagnosed as active AMD. CONCLUSION: SS-OCTA alone remains limited in some specific phenotypes of PCV, which suggests the ongoing role of B-scan OCT associated with FFA and ICGA in the diagnosis of these conditions.

Artificial intelligence-based predictions in neovascular age-related macular degeneration.

PURPOSE OF REVIEW: Predicting treatment response and optimizing treatment regimen in patients with neovascular age-related macular degeneration (nAMD) remains challenging. Artificial intelligence-based tools have the potential to increase confidence in clinical development of new therapeutics, facilitate individual prognostic predictions, and ultimately inform treatment decisions in clinical practice. RECENT FINDINGS: To date, most advances in applying artificial intelligence to nAMD have focused on facilitating image analysis, particularly for automated segmentation, extraction, and quantification of imaging-based features from optical coherence tomography (OCT) images. No studies in our literature search evaluated whether artificial intelligence could predict the treatment regimen required for an optimal visual response for an individual patient. Challenges identified for developing artificial intelligence-based models for nAMD include the limited number of large datasets with high-quality OCT data, limiting the patient populations included in model development; lack of counterfactual data to inform how individual patients may have fared with an alternative treatment strategy; and absence of OCT data standards, impairing the development of models usable across devices. SUMMARY: Artificial intelligence has the potential to enable powerful prognostic tools for a complex nAMD treatment landscape; however, additional work remains before these tools are applicable to informing treatment decisions for nAMD in clinical practice.

Predominantly Persistent Subretinal Fluid in the Comparison of Age-Related Macular Degeneration Treatments Trials.

OBJECTIVE: To describe predominantly persistent subretinal fluid (SRF) in eyes receiving ranibizumab or bevacizumab for neovascular age-related macular degeneration and to compare visual acuity (VA) to eyes with nonpersistent SRF. DESIGN: Cohort within randomized clinical trial. PARTICIPANTS: Comparison of Age-related Macular Degeneration Treatments Trials patients assigned to pro re nata treatment. METHODS: Graders evaluated monthly OCT scans for SRF. Predominantly persistent SRF through week 12 was defined as SRF at baseline and weeks 4, 8, and 12. Predominantly persistent SRF through 1 or 2 years was defined as SRF in 80% or more of visits by years 1 or 2, respectively. Linear regression models including baseline predictors of VA and predominantly persistent intraretinal fluid (IRF) were used to evaluate mean differences in vision outcomes. PRIMARY OUTCOME MEASURES: Predominantly persistent SRF through year 1, adjusted VA score and VA change, and foveal SRF thickness. RESULTS: Among 406 eyes with baseline SRF, SRF persisted in 108 eyes (26.6%) through week 12, in 94 eyes (23.2%) through year 1, and in 77 eyes (19.0%) through year 2. Adjusted VA means at year 1 were similar between eyes with predominantly persistent versus non persistent SRF by week 12 (68.1 vs. 70.2 letters; P = 0.18), year 1 (67.6 vs. 70.2 letters; P = 0.11), and year 2 (71.4 vs. 70.9 letters; P = 0.78). Adjusted changes in mean VA at year 1 were similar between eyes with predominantly persistent versus nonpersistent SRF by week 12 (6.3 vs. 7.6 letters; P = 0.38), year 1 (5.5 vs. 7.8 letters; P = 0.14), and year 2 (8.1 vs. 7.7 letters; P = 0.78). Among eyes with predominantly persistent SRF through year 1, foveal SRF was absent in 46 eyes (48.9%), thickness was 1 to 200 µm in 48 eyes (50.0%) and more than 200 µm in 1 eye (1.1%) at year 1. CONCLUSIONS: Eyes with predominantly persistent and nonpersistent SRF through week 12, year 1, or year 2 showed similar VA outcomes after adjustment for baseline covariates and persistent IRF. At the foveal center, predominantly persistent SRF was most commonly absent or present in small quantities.

Differentiating Exudative Macular Degeneration and Polypoidal Choroidal Vasculopathy Using OCT B-Scan.

PURPOSE: Although polypoidal choroidal vasculopathy (PCV) is best diagnosed with indocyanine green angiography (ICGA), ICGA is often unavailable or not ordered. OCT is widely available, and OCT B-scan can visualize polypoidal lesions diagnostic of PCV as inverted U-shaped elevations of the retinal pigment epithelium (RPE) with heterogeneous reflectivity and sometimes ring-shaped lesions within the polypoidal lesion. This study aims to differentiate findings between eyes diagnosed with PCV or typical exudative age-related macular degeneration (AMD) using ICGA and then compares findings noted on the OCT B-scan line scan in each group. DESIGN: Retrospective, chart review. METHODS: Clinical features of eyes with PCV and typical exudative AMD were compared by using ICGA. Eyes with PCV were evaluated for inverted U-shaped polypoidal lesions, which are the main differentiating finding of PCV from typical exudative AMD. Data collected included presence of subretinal fluid (SRF), macular edema or intraretinal edema, subretinal hyperreflective material (SHRM), and retinal pigment epithelial detachment (RPED). These findings were evaluated in 2 parts: baseline and after 6 to 9 months of antiangiogenic therapy. Additionally, analysis was performed for the presence of polypoidal lesions before and after treatment. MAIN OUTCOME MEASURES: Presence of inverted U-shaped lesions on OCT B-scan following treatment. RESULTS: A total of 112 eyes of 106 patients were included. A total of 69 eyes were diagnosed with PCV, and 43 eyes were diagnosed with typical exudative AMD. Compared with AMD eyes, PCV eyes had an increased prevalence of SRF at baseline and after 6 to 9 months of treatment, but the prevalence of macular edema, SHRM, and RPED was similar at baseline and at 6 to 9 months after treatment. In PCV eyes, the presence of visible polypoidal lesions decreased from 56.5% to 24.6% after treatment. CONCLUSIONS: If PCV is suspected in an anti-vascular endothelial growth factor (VEGF)-resistant case of exudative AMD, in the absence of ICGA availability, it is important to look at the baseline OCT B-scan before therapy for evidence of polypoidal lesions. The characteristic inverted U-shaped elevation was present in more than half of PCV eyes on OCT B-scan at baseline but disappeared after antiangiogenic therapy in 56.4% of cases in which this was initially identified. Subretinal fluid was more prevalent in PCV eyes than non-PCV AMD eyes.

Non-ICGA treatment criteria for Suboptimal Anti-VEGF Response for Polypoidal Choroidal Vasculopathy: APOIS PCV Workgroup Report 2.

PURPOSE: To develop and validate OCT and color fundus photography (CFP) criteria in differentiating polypoidal choroidal vasculopathy (PCV) from typical neovascular age-related macular degeneration (nAMD) in eyes with suboptimal response to anti-vascular endothelial growth factor (VEGF) monotherapy and to determine whether OCT alone can be used to guide photodynamic therapy (PDT) treatment. DESIGN: Clinical study evaluating diagnostic accuracy. PARTICIPANTS: Patients with nAMD who received 3-month anti-VEGF monotherapy but had persistent activity defined as subretinal fluid or intraretinal fluid at month 3 assessments. METHODS: In phase 1, international retina experts evaluated OCT and CFP of eyes with nAMD to identify the presence or absence of features due to PCV. The performance of individual and combinations of these features were compared with ICGA. In phase 2, these criteria were applied to an independent image set to assess generalizability. In a separate exercise, retinal experts drew proposed PDT treatment spots using only OCT and near-infrared (NIR) images in eyes with PCV and persistent activity. The location and size of proposed spot were compared with ICGA to determine the extent of coverage of polypoidal lesions (PLs) and branching neovascular network (BNN). MAIN OUTCOME MEASURES: Sensitivity and specificity of CFP and OCT criteria to differentiate PCV from nAMD and accuracy of coverage of OCT-guided PDT compared with ICGA. RESULTS: In eyes with persistent activity, the combination of 3 non-ICGA-based criteria (sharp-peaked pigment epithelial detachment [PED], subretinal pigment epithelium [RPE] ring-like lesion, and orange nodule) to detect PCV showed good agreement compared with ICGA, with an area under the receiver operating characteristic curve of 0.85. Validation using both an independent image set and assessors achieved an accuracy of 0.77. Compared with ICGA, the OCT-guided PDT treatment spot covered 100% of PL and 90% of the BNN. CONCLUSIONS: In nAMD eyes with persistent activity, OCT and CFP can differentiate PCV from typical nAMD, which may allow the option of adjunct PDT treatment. Furthermore, OCT alone can be used to plan adjunct PDT treatment without the need for ICGA, with consistent and complete coverage of PL.