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.

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.

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.

Segmentation of macular neovascularization and leakage in fluorescein angiography images in neovascular age-related macular degeneration using deep learning.

BACKGROUND/OBJECTIVES: We aim to develop an objective fully automated Artificial intelligence (AI) algorithm for MNV lesion size and leakage area segmentation on fluorescein angiography (FA) in patients with neovascular age-related macular degeneration (nAMD). SUBJECTS/METHODS: Two FA image datasets collected form large prospective multicentre trials consisting of 4710 images from 513 patients and 4558 images from 514 patients were used to develop and evaluate a deep learning-based algorithm to detect CNV lesion size and leakage area automatically. Manual segmentation of was performed by certified FA graders of the Vienna Reading Center. Precision, Recall and F1 score between AI predictions and manual annotations were computed. In addition, two masked retina experts conducted a clinical-applicability evaluation, comparing the quality of AI based and manual segmentations. RESULTS: For CNV lesion size and leakage area segmentation, we obtained F1 scores of 0.73 and 0.65, respectively. Expert review resulted in a slight preference for the automated segmentations in both datasets. The quality of automated segmentations was slightly more often judged as good compared to manual annotations. CONCLUSIONS: CNV lesion size and leakage area can be segmented by our automated model at human-level performance, its output being well-accepted during clinical applicability testing. The results provide proof-of-concept that an automated deep learning approach can improve efficacy of objective biomarker analysis in FA images and will be well-suited for clinical application.

A systematic evaluation of human expert agreement on optical coherence tomography biomarkers using multiple devices.

OBJECTIVES: To assess the agreement in evaluating optical coherence tomography (OCT) variables in the leading macular diseases such as neovascular age-related macular degeneration (nAMD), diabetic macular oedema (DMO) and retinal vein occlusion (RVO) among OCT-certified graders. METHODS: SD-OCT volume scans of 356 eyes were graded by seven graders. The grading included presence of intra- and subretinal fluid (IRF, SRF), pigment epithelial detachment (PED), epiretinal membrane (ERM), conditions of the vitreomacular interface (VMI), central retinal thickness (CRT) at the foveal centre-point (CP) and central millimetre (CMM), as well as height and location of IRF/SRF/PED. Kappa statistics (κ) and intraclass correlation coefficient (ICC) were used to report categorical grading and measurement agreement. RESULTS: The overall agreement on the presence of IRF/SRF/PED was κ = 0.82/0.85/0.81; κ of VMI condition was 0.77, that of ERM presence 0.37. ICC for CRT measurements at CP and CMM was excellent with an ICC of 1.00. Height measurements of IRF/SRF/PED showed robust consistency with ICC = 0.85-0.93. There was substantial to almost perfect agreement in locating IRF/SRF/PED with κ = 0.67-0.86. Between diseases, κ of IRF/SRF presence was 0.69/0.80 for nAMD, 0.64/0.83 for DMO and 0.86/0.89 for RVO. CONCLUSION: Even in the optimized setting, featuring certified graders, standardized image acquisition and the use of a professional reading platform, there is a disease dependent variability in biomarker evaluation that is most pronounced for IRF in nAMD as well as DMO. Our findings highlight the variability in the performance of human expert OCT grading and the need for AI-based automated feature analyses.

Quality assessment of colour fundus and fluorescein angiography images using deep learning.

BACKGROUND/AIMS: Image quality assessment (IQA) is crucial for both reading centres in clinical studies and routine practice, as only adequate quality allows clinicians to correctly identify diseases and treat patients accordingly. Here we aim to develop a neural network for automated real-time IQA in colour fundus (CF) and fluorescein angiography (FA) images. METHODS: Training and evaluation of two neural networks were conducted using 2272 CF and 2492 FA images, with binary labels in four (contrast, focus, illumination, shadow and reflection) and three (contrast, focus, noise) modality specific categories plus an overall quality ranking. Performance was compared with a second human grader, evaluated on an external public dataset and in a clinical trial use-case. RESULTS: The networks achieved a F1-score/area under the receiving operator characteristic/precision recall curve of 0.907/0.963/0.966 for CF and 0.822/0.918/0.889 for FA in overall quality prediction with similar results in most categories. A clear relation between model uncertainty and prediction error was observed. In the clinical trial use-case evaluation, the networks achieved an accuracy of 0.930 for CF and 0.895 for FA. CONCLUSION: The presented method allows automated IQA in real time, demonstrating human-level performance for CF as well as FA. Such models can help to overcome the problem of human intergrader and intragrader variability by providing objective and reproducible IQA results. It has particular relevance for real-time feedback in multicentre clinical studies, when images are uploaded to central reading centre portals. Moreover, automated IQA as preprocessing step can support integrating automated approaches into clinical practice.

THE IMPACT OF THE VITREOMACULAR INTERFACE ON FUNCTIONAL AND ANATOMICAL OUTCOMES IN DIABETIC MACULAR EDEMA TREATED WITH THREE DIFFERENT ANTI-VEGF AGENTS: Post Hoc Analysis of The Protocol T Study.

PURPOSE: To investigate the impact of baseline vitreomacular interface status on treatment outcomes in patients treated with three different anti-vascular endothelial growth factors for diabetic macular edema. METHODS: Post hoc analysis from patients enrolled in the DRCR.net Protocol T study. Optical coherence tomography images were analyzed at baseline and at the end of follow-up to identify the presence of complete vitreomacular adhesion, partial vitreomacular adhesion, vitreomacular traction syndrome, and complete posterior vitreous detachment. RESULTS: Six hundred and twenty-nine eyes were eligible for the study based on the study criteria. Complete adhesion eyes gained on average +3.7 more ETDRS letters compared with the complete posterior vitreous detachment group at the end of the 12 months follow-up ( P < 0.001). Baseline vitreomacular interface status had no significant influence on central subfield thickness at 12 months ( P = 0.144). There was no difference between the treatment arms based on effect of baseline vitreomacular interface status on best-corrected visual acuity gain. CONCLUSION: This study provides evidence that vitreomacular interface status affects functional outcomes in diabetic macular edema patients treated with anti-vascular endothelial growth factor injections. The presence of complete or partial vitreomacular adhesion at baseline may be associated with a larger treatment benefit than those with complete posterior vitreous detachment.

VALIDATION OF AN AUTOMATED FLUID ALGORITHM ON REAL-WORLD DATA OF NEOVASCULAR AGE-RELATED MACULAR DEGENERATION OVER FIVE YEARS.

BACKGROUND/PURPOSE: To apply an automated deep learning automated fluid algorithm on data from real-world management of patients with neovascular age-related macular degeneration for quantification of intraretinal/subretinal fluid volumes in optical coherence tomography images. METHODS: Data from the Vienna Imaging Biomarker Eye Study (VIBES, 2007-2018) were analyzed. Databases were filtered for treatment-naive neovascular age-related macular degeneration with a baseline optical coherence tomography and at least one follow-up and 1,127 eyes included. Visual acuity and optical coherence tomography at baseline, Months 1 to 3/Years 1 to 5, age, sex, and treatment number were included. Artificial intelligence and certified manual grading were compared in a subanalysis of 20%. Main outcome measures were fluid volumes. RESULTS: Intraretinal/subretinal fluid volumes were maximum at baseline (intraretinal fluid: 21.5/76.6/107.1 nL; subretinal fluid 13.7/86/262.5 nL in the 1/3/6-mm area). Intraretinal fluid decreased to 5 nL at M1-M3 (1-mm) and increased to 11 nL (Y1) and 16 nL (Y5). Subretinal fluid decreased to a mean of 4 nL at M1-M3 (1-mm) and remained stable below 7 nL until Y5. Intraretinal fluid was the only variable that reflected VA change over time. Comparison with human expert readings confirmed an area under the curve of >0.9. CONCLUSION: The Vienna Fluid Monitor can precisely quantify fluid volumes in optical coherence tomography images from clinical routine over 5 years. Automated tools will introduce precision medicine based on fluid guidance into real-world management of exudative disease, improving clinical outcomes while saving resources.

Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures.

In clinical routine, ophthalmologists frequently analyze the shape and size of the foveal avascular zone (FAZ) to detect and monitor retinal diseases. In order to extract those parameters, the contours of the FAZ need to be segmented, which is normally achieved by analyzing the retinal vasculature (RV) around the macula in fluorescein angiograms (FA). Computer-aided segmentation methods based on deep learning (DL) can automate this task. However, current approaches for segmenting the FAZ are often tailored to a specific dataset or require manual initialization. Furthermore, they do not take the variability and challenges of clinical FA into account, which are often of low quality and difficult to analyze. In this paper we propose a DL-based framework to automatically segment the FAZ in challenging FA scans from clinical routine. Our approach mimics the workflow of retinal experts by using additional RV labels as a guidance during training. Hence, our model is able to produce RV segmentations simultaneously. We minimize the annotation work by using a multi-modal approach that leverages already available public datasets of color fundus pictures (CFPs) and their respective manual RV labels. Our experimental evaluation on two datasets with FA from 1) clinical routine and 2) large multicenter clinical trials shows that the addition of weak RV labels as a guidance during training improves the FAZ segmentation significantly with respect to using only manual FAZ annotations.

Association of microvascular biomarkers in fluorescein angiography with macrovascular-related mortality in clinical routine data.

PURPOSE: Early detection of microvascular changes in the retina may be important for the risk assessment of cardiovascular health. Therefore, the purpose of this study was to investigate imaging biomarkers in fluorescein angiography (FA) as potential predictors for cardiovascular mortality. METHODS: In this retrospective, matched case-control study, we included FA images from clinical routine data between 2007 and 2018 of 100 patients who died of macrovascular events (Group 1) and 100 age- and sex-matched controls (Group 2). All patients were under treatment for different, mostly retinal, ocular diseases. FA images were used for the measurement of the foveal avascular zone (FAZ) and the arteriolar and venular caliber. RESULTS: Patients mean age on examination day was 69.5 ± 8.3 years with a 1:1 female:male subject ratio. Mean FAZ area of our sample was 0.340 ± 0.135 mm2 for Group 1 and 0.264 ± 0.137 mm2 for Group 2 (P < 0.001), showing a larger FAZ area in patients who subsequently died of macrovascular-related systemic diseases. CONCLUSIONS: Individuals effected by a macrovascular-related disease show a larger FAZ on FA examinations before the event compared to patients which are unaffected. Our results highlight a possible role of the FAZ as additional biomarker for the cardiovascular condition.

Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.

The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central vision. The localization of this anatomical landmark in optical coherence tomography (OCT) volumes is important for assessing visual function correlates and treatment guidance in macular disease. In this study, the "PRE U-net" is introduced as a novel approach for a fully automated fovea centralis detection, addressing the localization as a pixel-wise regression task. 2D B-scans are sampled from each image volume and are concatenated with spatial location information to train the deep network. A total of 5586 OCT volumes from 1,541 eyes were used to train, validate and test the deep learning method. The test data is comprised of healthy subjects and patients affected by neovascular age-related macular degeneration (nAMD), diabetic macula edema (DME) and macular edema from retinal vein occlusion (RVO), covering the three major retinal diseases responsible for blindness. Our experiments demonstrate that the PRE U-net significantly outperforms state-of-the-art methods and improves the robustness of automated localization, which is of value for clinical practice.

Comparison of early diabetic retinopathy staging in asymptomatic patients between autonomous AI-based screening and human-graded ultra-widefield colour fundus images.

INTRODUCTION: Comparison of diabetic retinopathy (DR) severity between autonomous Artificial Intelligence (AI)-based outputs from an FDA-approved screening system and human retina specialists' gradings from ultra-widefield (UWF) colour images. METHODS: Asymptomatic diabetics without a previous diagnosis of DR were included in this prospective observational pilot study. Patients were imaged with autonomous AI (IDx-DR, Digital Diagnostics). For each eye, two 45° colour fundus images were analysed by a secure server-based AI algorithm. UWF colour fundus imaging was performed using Optomap (Daytona, Optos). The International Clinical DR severity score was assessed both on a 7-field area projection (7F-mask) according to the early treatment diabetic retinopathy study (ETDRS) and on the total gradable area (UWF full-field) up to the far periphery on UWF images. RESULTS: Of 54 patients included (n = 107 eyes), 32 were type 2 diabetics (11 females). Mean BCVA was 0.99 ± 0.25. Autonomous AI diagnosed 16 patients as negative, 28 for moderate DR and 10 for having a vision-threatening disease (severe DR, proliferative DR, diabetic macular oedema). Based on the 7F-mask grading with the eye with the worse grading defining the DR stage 23 patients were negative for DR, 11 showed mild, 19 moderate and 1 severe DR. When UWF full-field was analysed, 20 patients were negative for DR, while the number of mild, moderate and severe DR patients were 12, 21, and 1, respectively. CONCLUSIONS: The autonomous AI-based DR examination demonstrates sufficient accuracy in diagnosing asymptomatic non-proliferative diabetic patients with referable DR even compared to UWF imaging evaluated by human experts offering a suitable method for DR screening.

THE RAP STUDY, REPORT 5: REDISCOVERING MACULAR NEOVASCULARIZATION TYPE 3: Multimodal Imaging of Fellow Eyes over 24 months.

PURPOSE: To explore the condition of fellow eyes of patients with macular neovascularization Type 3 (MNV3) and to verify whether the retinal-choroidal anastomosis (RCA) develops equally in all MNV types. METHODS: The contralateral eyes of 94 patients with MNV3, 96 patients with MNV1, and 96 patients with MNV2 were included. Multimodal imaging was performed. The MNV3 stage including the development of fibrosis and RCA over 24 months was determined. RESULTS: In the contralateral eyes of patients of the solitary (one lesion) MNV3 group, 32 eyes (42.1%) showed early/intermediate age-related macular degeneration, 25 eyes (33%) showed MNV3, and 11 eyes (14.5%) experienced fibrosis, of which 4 eyes (5.2%) had a RCA, 7 eyes (9.2%) had atrophy after resolved MNV3, and 1 eye (1.3%) developed MNV1. In the multifocal (more than one lesion) MNV3 group, 2 eyes (11.1%) showed early/intermediate age-related macular degeneration, 9 eyes (50%) showed 15 MNV3 lesions, and 4 eyes (22.2%) showed fibrosis, of which 2 eyes (11.1%) manifested with a RCA and 3 eyes (16.7%) showed atrophy after resolved MNV3. The number of eyes with a RCA accounted for 40% of all eyes with fibrosis. The count of simultaneous bilateral multifocal MNV3 was 5 (55.6%). In the MNV1 and MNV2 groups, no eye developed a RCA. The incidence of RCAs in the scarred eyes in MNV3 was significantly higher (P < 0.0001). CONCLUSION: Retinal-choroidal anastomosis is an exclusive clinical feature of MNV3. The development of the multifocal MNV3 is usually bilateral and simultaneous. The occurrence of fibrosis in MNV3 has decreased dramatically after the introduction of the antiangiogenic therapy.

Automated quantification of macular fluid in retinal diseases and their response to anti-VEGF therapy.

AIM: To objectively assess disease activity and treatment response in patients with retinal vein occlusion (RVO), neovascular age-related macular degeneration (nAMD) and centre-involved diabetic macular oedema (DME), using artificial intelligence-based fluid quantification. METHODS: Posthoc analysis of 2311 patients (11 151 spectral-domain optical coherence tomography volumes) from five clinical, multicentre trials, who received a flexible antivascular endothelial growth factor (anti-VEGF) therapy over a 12-month period. Fluid volumes were measured with a deep learning algorithm at baseline/months 1, 2, 3 and 12, for three concentric circles with diameters of 1, 3 and 6 mm (fovea, paracentral ring and pericentral ring), as well as four sectors surrounding the fovea (superior, nasal, inferior and temporal). RESULTS: In each disease, at every timepoint, most intraretinal fluid (IRF) per square millimetre was present at the fovea, followed by the paracentral ring and pericentral ring (p<0.0001). While this was also the case for subretinal fluid (SRF) in RVO/DME (p<0.0001), patients with nAMD showed more SRF in the paracentral ring than at the fovea up to month 3 (p<0.0001). Between sectors, patients with RVO/DME showed the highest IRF volumes temporally (p<0.001/p<0.0001). In each disease, more SRF was consistently found inferiorly than superiorly (p<0.02). At month 1/12, we measured the following median reductions of initial fluid volumes. For IRF: RVO, 95.9%/97.7%; nAMD, 91.3%/92.8%; DME, 37.3%/69.9%. For SRF: RVO, 94.7%/97.5%; nAMD, 98.4%/99.8%; DME, 86.3%/97.5%. CONCLUSION: Fully automated localisation and quantification of IRF/SRF over time shed light on the fluid dynamics in each disease. There is a specific anatomical response of IRF/SRF to anti-VEGF therapy in all diseases studied.

Microvascular abnormalities and long-term efficacy after stereotactic radiotherapy under continued intravitreal anti-VEGF treatment for neovascular AMD.

BACKGROUND: For treatment of neovascular age-related macular degeneration (nAMD), multiple intravitreal injections of drugs targeting vascular endothelial growth factors (VEGF) result in a high burden for patients and healthcare systems. Low-energy stereotactic radiotherapy (SRT) might reduce the anti-VEGF need. This study evaluated the long-term efficacy and safety of adjunct SRT to anti-VEGF injections in a treat-and-extend regimen in nAMD. METHODS: 50 consecutive patients were followed 3 years after single-session SRT, a safety analysis including standardised study imaging, and a reading centre based image analysis was performed after 2 years. RESULTS: After increase from baseline (4.24±0.66 weeks) to 12 months (7.52±3.05 weeks, p<0.001), mean recurrence-free anti-VEGF treatment interval remained stable at 24 (7.40±3.17, p=0.746) and 36 months (6.89±3.00, p=0.175). Mean visual acuity change was -5.8±15.9 and -11.0±20.1 letters at 24 and 36 months, respectively. 36% of eyes showed microvascular abnormalities (MVAs) on colour fundus photography and/or fluoresceine angiography most frequently located in parafoveal inferior and nasal regions. CONCLUSION: In real life, low-energy SRT was associated with a reduced anti-VEGF injection frequency through year 3. However, due to an observed visual acuity reduction and remarkable number of MVAs, a close follow-up of these patients is recommended. The real-life use, optimal treatment schedule and dose should be rediscussed critically.

The RAP study, report 3: Discoloration of the macular region in patients with macular neovascularization type 3.

BACKGROUND/AIMS: To explore whether the existence and pattern of distribution of macular haemorrhage or exudate can be valuable diagnostic markers for macular neovascularization type 3 (MNV3) in patients with neovascular age-related macular degeneration. METHODS: Eighty-three eyes of 83 consecutive treatment naïve patients with stage 3 MNV3 were enrolled. The diagnosis was based on fluorescein angiography (FA) and optical coherence tomography (OCT). Subretinal and intraretinal haemorrhage and dense exudates were evaluated on colour fundus photography. Fluorescein angiography (FA) images and OCT scans were used to identify the axial location of the haemorrhage. 83 patients with MNV1 and 83 with MNV2 were included as two control groups. RESULTS: In the MNV3 group, 62 (75%) eyes had intraretinal haemorrhage and 52 (63%) had dense exudates. 73 (88%) eyes had intraretinal haemorrhage and/or dense exudates. 41 (49%) had both pathologies. The intraretinal haemorrhage was flame shaped over the lesion and punctate or semi-punctate further away from it and directed to the fovea. No subretinal haemorrhage was noticed. In the MNV1 and MNV2 groups, 11 (13%) and 24 (29%) eyes had subretinal haemorrhage or dense exudates, respectively. No intraretinal haemorrhage was seen in the two control groups. The prevalence of exudates and haemorrhage (irrespective of its location) was greater in MNV3 than in MNV1 or 2 (p < 0.0001). CONCLUSION: The existence and pattern of distribution of intraretinal haemorrhage is pathognomonic of MNV3. It makes (alone or with dense exudates) the diagnose MNV3 possible using fundoscopy or colour fundus photo and without further diagnostic expenditure.