Towards standardising retinal OCT angiography image analysis with open-source toolbox OCTAVA.

Quantitative assessment of retinal microvasculature in optical coherence tomography angiography (OCTA) images is important for studying, diagnosing, monitoring, and guiding the treatment of ocular and systemic diseases. However, the OCTA user community lacks universal and transparent image analysis tools that can be applied to images from a range of OCTA instruments and provide reliable and consistent microvascular metrics from diverse datasets. We present a retinal extension to the OCTA Vascular Analyser (OCTAVA) that addresses the challenges of providing robust, easy-to-use, and transparent analysis of retinal OCTA images. OCTAVA is a user-friendly, open-source toolbox that can analyse retinal OCTA images from various instruments. The toolbox delivers seven microvascular metrics for the whole image or subregions and six metrics characterising the foveal avascular zone. We validate OCTAVA using images collected by four commercial OCTA instruments demonstrating robust performance across datasets from different instruments acquired at different sites from different study cohorts. We show that OCTAVA delivers values for retinal microvascular metrics comparable to the literature and reduces their variation between studies compared to their commercial equivalents. By making OCTAVA publicly available, we aim to expand standardised research and thereby improve the reproducibility of quantitative analysis of retinal microvascular imaging. Such improvements will help to better identify more reliable and sensitive biomarkers of ocular and systemic diseases.

Polarization-Diversity Optical Coherence Tomography Assessment of Choroidal Nevi.

Purpose: The purpose of this study was to demonstrate the utility of polarization-diversity optical coherence tomography (PD-OCT), a noninvasive imaging technique with melanin-specific contrast, in the quantitative and qualitative assessment of choroidal nevi. Methods: Nevi were imaged with a custom-built 55-degree field-of-view (FOV) 400 kHz PD-OCT system. Imaging features on PD-OCT were compared to those on fundus photography, auto-fluorescence, ultrasound, and non-PD-OCT images. Lesions were manually segmented for size measurement and metrics for objective assessment of melanin distributions were calculated, including degree of polarization uniformity (DOPU), attenuation coefficient, and melanin occupancy rate (MOR). Results: We imaged 17 patients (mean age = 69.5 years, range = 37-90) with 11 pigmented, 3 non-pigmented, and 3 mixed pigmentation nevi. Nevi with full margin acquisition had an average longest basal diameter of 5.1 mm (range = 2.99-8.72 mm) and average height of 0.72 mm (range = 0.37 mm-2.09 mm). PD-OCT provided clear contrast of choroidal melanin content, distribution, and delineation of nevus margins for melanotic nevi. Pigmented nevi were found to have lower DOPU, higher attenuation coefficient, and higher MOR than non-pigmented lesions. Melanin content on PD-OCT was consistent with pigmentation on fundus in 15 of 17 nevi (88%). Conclusions: PD-OCT allows objective assessment of choroidal nevi melanin content and distribution. In addition, melanin-specific contrast by PD-OCT enables clear nevus margin delineation and may improve serial growth surveillance. Further investigation is needed to determine the clinical significance and prognostic value of melanin characterization by PD-OCT in the evaluation of choroidal nevi.

Segmentation-guided domain adaptation and data harmonization of multi-device retinal optical coherence tomography using cycle-consistent generative adversarial networks.

BACKGROUND: Medical images such as Optical Coherence Tomography (OCT) images acquired from different devices may show significantly different intensity profiles. An automatic segmentation model trained on images from one device may perform poorly when applied to images acquired using another device, resulting in a lack of generalizability. This study addresses this issue using domain adaptation methods improved by Cycle-Consistent Generative Adversarial Networks (CycleGAN), especially when the ground-truth labels are only available in the source domain. METHODS: A two-stage pipeline is proposed to generate segmentation in the target domain. The first stage involves the training of a state-of-the-art segmentation model in the source domain. The second stage aims to adapt the images from the target domain to the source domain. The adapted target domain images are segmented using the model in the first stage. Ablation tests were performed with integration of different loss functions, and the statistical significance of these models is reported. Both the segmentation performance and the adapted image quality metrics were evaluated. RESULTS: Regarding the segmentation Dice score, the proposed model ssppg achieves a significant improvement of 46.24% compared to without adaptation and reaches 87.4% of the upper limit of the segmentation performance. Furthermore, image quality metrics, including FID and KID scores, indicate that adapted images with better segmentation also have better image qualities. CONCLUSION: The proposed method demonstrates the effectiveness of segmentation-driven domain adaptation in retinal imaging processing. It reduces the labor cost of manual labeling, incorporates prior anatomic information to regulate and guide domain adaptation, and provides insights into improving segmentation qualities in image domains without labels.

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.

Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.

Pilot study of optical coherence tomography angiography-derived microvascular metrics in hands and feet of healthy and diabetic people.

Optical coherence tomography angiography (OCTA) is a non-invasive, high-resolution imaging modality with growing application in dermatology and microvascular assessment. Accepted reference values for OCTA-derived microvascular parameters in skin do not yet exist but need to be established to drive OCTA into the clinic. In this pilot study, we assess a range of OCTA microvascular metrics at rest and after post-occlusive reactive hyperaemia (PORH) in the hands and feet of 52 healthy people and 11 people with well-controlled type 2 diabetes mellitus (T2DM). We calculate each metric, measure test-retest repeatability, and evaluate correlation with demographic risk factors. Our study delivers extremity-specific, age-dependent reference values and coefficients of repeatability of nine microvascular metrics at baseline and at the maximum of PORH. Significant differences are not seen for age-dependent microvascular metrics in hand, but they are present for several metrics in the foot. Significant differences are observed between hand and foot, both at baseline and maximum PORH, for most of the microvascular metrics with generally higher values in the hand. Despite a large variability over a range of individuals, as is expected based on heterogeneous ageing phenotypes of the population, the test-retest repeatability is 3.5% to 18% of the mean value for all metrics, which highlights the opportunities for OCTA-based studies in larger cohorts, for longitudinal monitoring, and for assessing the efficacy of interventions. Additionally, branchpoint density in the hand and foot and changes in vessel diameter in response to PORH stood out as good discriminators between healthy and T2DM groups, which indicates their potential value as biomarkers. This study, building on our previous work, represents a further step towards standardised OCTA in clinical practice and research.

Adaptive optics scanning laser ophthalmoscopy and optical coherence tomography (AO-SLO-OCT) system for in vivo mouse retina imaging.

Optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO) are imaging technologies invented in the 1980s that have revolutionized the field of in vivo retinal diagnostics and are now commonly used in ophthalmology clinics as well as in vision science research. Adaptive optics (AO) technology enables high-fidelity correction of ocular aberrations, resulting in improved resolution and sensitivity for both SLO and OCT systems. The potential of gathering multi-modal cellular-resolution information in a single instrument is of great interest to the ophthalmic imaging community. Although similar instruments have been developed for imaging the human retina, developing such a system for mice will benefit basic science research and should help with further dissemination of AO technology. Here, we present our work integrating OCT into an existing mouse retinal AO-SLO system, resulting in a multi-modal AO-enhanced imaging system of the living mouse eye. The new system allows either independent or simultaneous data acquisition of AO-SLO and AO-OCT, depending on the requirements of specific scientific experiments. The system allows a data acquisition speed of 200 kHz A-scans/pixel rate for OCT and SLO, respectively. It offers ∼6 µm axial resolution for AO-OCT and a ∼1 µm lateral resolution for AO-SLO-OCT imaging.

Effects of Myopia and Glaucoma on the Neural Canal and Lamina Cribrosa Using Optical Coherence Tomography.

PRCIS: Glaucoma was associated with axial bowing and rotation of Bruchs membrane opening (BMO) and anterior laminar insertion (ALI), skewed neural canal, and deeper anterior lamina cribrosa surface (ALCS). Longer axial length was associated with wider, longer, and more skewed neural canal and flatter ALCS. PURPOSE: Investigate the effects of myopia and glaucoma in the prelaminar neural canal and anterior lamina cribrosa using 1060-nm swept-source optical coherence tomography. PATIENTS: 19 control (38 eyes) and 38 glaucomatous subjects (63 eyes). MATERIALS AND METHODS: Participants were imaged with swept-source optical coherence tomography, and the images were analyzed for the BMO and ALI dimensions, prelaminar neural canal dimensions, and ALCS depth. RESULTS: Glaucomatous eyes had more bowed and nasally rotated BMO and ALI, more horizontally skewed prelaminar neural canal, and deeper ALCS than the control eyes. Increased axial length was associated with a wider, longer, and more horizontally skewed neural canal and a decrease in the ALCS depth and curvature. CONCLUSION: Our findings suggest that glaucomatous posterior bowing or cupping of lamina cribrosa can be significantly confounded by the myopic expansion of the neural canal. This may be related to higher glaucoma risk associated with myopia from decreased compliance and increased susceptibility to IOP-related damage of LC being pulled taut.

Numerical calibration method for a multiple spectrometer-based OCT system.

The present paper introduces a numerical calibration method for the easy and practical implementation of multiple spectrometer-based spectral-domain optical coherence tomography (SD-OCT) systems. To address the limitations of the traditional hardware-based spectrometer alignment across more than one spectrometer, we applied a numerical spectral calibration algorithm where the pixels corresponding to the same wavelength in each unit are identified through spatial- and frequency-domain interferometric signatures of a mirror sample. The utility of dual spectrometer-based SD-OCT imaging is demonstrated through in vivo retinal imaging at two different operation modes with high-speed and dual balanced acquisitions, respectively, in which the spectral alignment is critical to achieve improved retinal image data without any artifacts caused by misalignment of the spectrometers.

Clinical explainable differential diagnosis of polypoidal choroidal vasculopathy and age-related macular degeneration using deep learning.

BACKGROUND: This study aims to achieve an automatic differential diagnosis between two types of retinal pathologies with similar pathological features - Polypoidal choroidal vasculopathy (PCV) and wet age-related macular degeneration (AMD) from volumetric optical coherence tomography (OCT) images, and identify clinically-relevant pathological features, using an explainable deep-learning-based framework. METHODS: This is a retrospective study with data from a cross-sectional cohort. The OCT volume of 73 eyes from 59 patients was included in this study. Disease differentiation was achieved through single-B-scan-based classification followed by a volumetric probability prediction aggregation step. We compared different labeling strategies with and without identifying pathological B-scans within each OCT volume. Clinical interpretability was achieved through normalized aggregation of B-scan-based saliency maps followed by maximum-intensity-projection onto the en face plane. We derived the PCV score from the proposed differential diagnosis framework with different labeling strategies. The en face projection of saliency map was validated with the pathologies identified in Indocyanine green angiography (ICGA). RESULTS: Model trained with both labeling strategies achieved similar level differentiation power (>90%), with good correspondence between pathological features detected from the projected en face saliency map and ICGA. CONCLUSIONS: This study demonstrated the potential clinical application of non-invasive differential diagnosis using AI-driven OCT-based analysis, with minimal requirement of labeling efforts, along with clinical explainability achieved through automatically detected disease-related pathologies.

Quantitative Optical Coherence Tomography Angiography in Patients with Moyamoya Vasculopathy: A Pilot Study.

Moyamoya (MM) disease is a chronic cerebrovascular disease that can lead to progressive stenosis of the terminal portions of the internal carotid arteries and their proximal branches. We sought to investigate and quantify retinal vascular changes in patients with MM vasculopathy (MMV) using optical coherence tomography angiography (OCTA) compared to healthy controls. Our findings reveal retinal microvascular changes in patients with MMV and highlights the potential of OCTA imaging for the detection of subclinical retinal pathology.

Effect of optical coherence tomography and angiography sampling rate towards diabetic retinopathy severity classification.

Optical coherence tomography (OCT) and OCT angiography (OCT-A) may benefit the screening of diabetic retinopathy (DR). This study investigated the effect of laterally subsampling OCT/OCT-A en face scans by up to a factor of 8 when using deep neural networks for automated referable DR classification. There was no significant difference in the classification performance across all evaluation metrics when subsampling up to a factor of 3, and only minimal differences up to a factor of 8. Our findings suggest that OCT/OCT-A can reduce the number of samples (and hence the acquisition time) for a volume for a given field of view on the retina that is acquired for rDR classification.

Wavefront sensor-less adaptive optics using deep reinforcement learning.

Image degradation due to wavefront aberrations can be corrected with adaptive optics (AO). In a typical AO configuration, the aberrations are measured directly using a Shack-Hartmann wavefront sensor and corrected with a deformable mirror in order to attain diffraction limited performance for the main imaging system. Wavefront sensor-less adaptive optics (SAO) uses the image information directly to determine the aberrations and provide guidance for shaping the deformable mirror, often iteratively. In this report, we present a Deep Reinforcement Learning (DRL) approach for SAO correction using a custom-built fluorescence confocal scanning laser microscope. The experimental results demonstrate the improved performance of the DRL approach relative to a Zernike Mode Hill Climbing algorithm for SAO.

LF-UNet - A novel anatomical-aware dual-branch cascaded deep neural network for segmentation of retinal layers and fluid from optical coherence tomography images.

Computer-assistant diagnosis of retinal disease relies heavily on the accurate detection of retinal boundaries and other pathological features such as fluid accumulation. Optical coherence tomography (OCT) is a non-invasive ophthalmological imaging technique that has become a standard modality in the field due to its ability to detect cross-sectional retinal pathologies at the micrometer level. In this work, we presented a novel framework to achieve simultaneous retinal layers and fluid segmentation. A dual-branch deep neural network, termed LF-UNet, was proposed which combines the expansion path of the U-Net and original fully convolutional network, with a dilated network. In addition, we introduced a cascaded network framework to include the anatomical awareness embedded in the volumetric image. Cross validation experiments showed that the proposed LF-UNet has superior performance compared to the state-of-the-art methods, and that incorporating the relative positional map structural prior information could further improve the performance regardless of the network. The generalizability of the proposed network was demonstrated on an independent dataset acquired from the same types of device with different field of view, or images acquired from different device.

Therapeutic Neuroprotection by an Engineered Neurotrophin that Selectively Activates Tropomyosin Receptor Kinase (Trk) Family Neurotrophin Receptors but Not the p75 Neurotrophin Receptor.

The neurotrophin growth factors bind and activate two types of cell surface receptors: the tropomyosin receptor kinase (Trk) family and p75. TrkA, TrkB, and TrkC are bound preferentially by nerve growth factor, brain-derived neurotrophic factor, and neurotrophin 3 (NT3), respectively, to activate neuroprotective signals. The p75 receptors are activated by all neurotrophins, and paradoxically in neurodegenerative disease p75 is upregulated and mediates neurotoxic signals. To test neuroprotection strategies, we engineered NT3 to broadly activate Trk receptors (mutant D) or to reduce p75 binding (mutant RK). We also combined these features in a molecule that activates TrkA, TrkB, and TrkC but has reduced p75 binding (mutant DRK). In neurodegenerative disease mouse models in vivo, the DRK protein is a superior therapeutic agent compared with mutant D, mutant RK, and wild-type neurotrophins and protects a broader range of stressed neurons. This work rationalizes a therapeutic strategy based on the biology of each type of receptor, avoiding activation of p75 toxicity while broadly activating neuroprotection in stressed neuronal populations expressing different Trk receptors. SIGNIFICANCE STATEMENT: The neurotrophins nerve growth factor, brain-derived neurotrophic factor, and neurotrophin 3 each can activate a tropomyosin receptor kinase (Trk) A, TrkB, or TrkC receptor, respectively, and all can activate a p75 receptor. Trks and p75 mediate opposite signals. We report the engineering of a protein that activates all Trks, combined with low p75 binding, as an effective therapeutic agent in vivo.

Phase-corrected buffer averaging for enhanced OCT angiography using FDML laser.

Megahertz-rate optical coherence tomography angiography (OCTA) is highly anticipated as an ultrafast imaging tool in clinical settings. However, shot-noise-limited sensitivity is inevitably reduced in high-speed imaging systems. In this Letter, we present a coherent buffer averaging technique for use with a Fourier-domain mode-locked (FDML) laser to improve OCTA contrast at 1060 nm MHz-rate retinal imaging. Full characterization of spectral variations among the FDML buffers and a numerical correction method are also presented, with the results demonstrating a 10-fold increase in the phase alignment among buffers. Coherent buffer averaging provided better OCTA contrast than the conventional multi-frame averaging approach with a faster acquisition time.

Investigation of the Peripapillary Choriocapillaris in Normal Tension Glaucoma, Primary Open-angle Glaucoma, and Control Eyes.

PRECIS: The peripapillary choriocapillaris (CC) was observed to be significantly impaired in normal tension glaucoma (NTG) subjects compared with normal controls using optical coherence tomography angiography (OCTA). PURPOSE: The aim was to quantitatively evaluate the peripapillary CC in NTG, primary open-angle glaucoma (POAG), and control eyes using OCTA. MATERIALS AND METHODS: Ninety eyes (30 controls, 30 NTG, and 30 POAG) from 73 patients were imaged using the Zeiss Plex Elite 9000. Five repeat 3×3 mm OCTA scans were acquired both nasally and temporally to the optic disc and subsequently averaged. Four CC flow deficit (FD) measures were calculated using the fuzzy C-means approach: FD density (FDD), mean FD size (MFDS), FD number (FDN), and FD area (FDA). RESULTS: Temporal NTG CC parameters were associated with visual field index and mean deviation (P<0.05). The control group showed a significantly lower nasal FDD (nasal: 3.79±1.26%, temporal: 4.48±1.73%, P=0.03), FDN (nasal: 156.43±38.44, temporal: 178.40±45.68, P=0.02), and FDA (nasal: 0.22±0.08, temporal: 0.26±0.10, P=0.03) when compared with temporal optic disc. The NTG group showed a significantly higher FDD (NTG: 5.04±2.38%, control: 3.79±1.26%, P=0.03), FDN (NTG: 185.90±56.66, control: 156.43±38.44, P=0.04), and FDA (NTG: 0.30±0.14 mm2, control: 0.22±0.08 mm2, P=0.03) nasal to the optic disc compared with controls. CONCLUSIONS: Association between CC parameters and glaucoma severity in NTG, but not POAG subjects, suggests vascular abnormalities may be a potential factor in the multifactorial process of glaucoma damage in NTG patients.

INVESTIGATING MICROANGIOPATHY USING SWEPT-SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IN PATIENTS WITH SUSAC SYNDROME.

PURPOSE: To determine whether optical coherence tomography angiography is of diagnostic utility for Susac syndrome (SuS) by quantifying microvascular retinal changes. METHODS: We enrolled 18 eyes of 9 healthy controls and 18 eyes of 9 patients with chronic SuS (12 had previous branch retinal artery occlusions and 6 were clinically unaffected). Images of the fovea were taken using an optical coherence tomography angiography system. Analysis included vessel density, fractal dimension, vessel diameter, and measurements of the foveal avascular zone (area, eccentricity, acircularity index, and axis ratio) in deep and superficial retinal layers. RESULTS: Skeleton density and inner ring vessel density were significantly lower in patients with SuS (skeleton density: Susac 0.11 ± 0.01 vs. controls 0.12 ± 0.01, P = 0.027. VD: SuS 0.39 ± 0.04 vs. controls 0.42 ± 0.02, P = 0.041). Eccentricity and axis ratio were significantly higher in patients with SuS (EC: Susac 0.61 ± 0.11, controls 0.51 ± 0.10, P = 0.003; axis ratio: Susac 1.57 ± 0.28, controls 1.39 ± 0.11, P = 0.005). SuS eyes (affected and unaffected) had poorer outcomes of the remaining vascular parameters compared with controls (P > 0.05). CONCLUSION: Optical coherence tomography angiography identified chronic microvascular changes in the eyes of patients with chronic SuS. Even clinically unaffected SuS eyes showed poorer vascular parameters. Although further research is needed, this noninvasive imaging modality seems to have the potential to serve as a valuable additive diagnostic tool.

Optimizing 3D retinal vasculature imaging in diabetic retinopathy using registration and averaging of OCT-A.

High resolution visualization of optical coherence tomography (OCT) and OCT angiography (OCT-A) data is required to fully take advantage of the imaging modality's three-dimensional nature. However, artifacts induced by patient motion often degrade OCT-A data quality. This is especially true for patients with deteriorated focal vision, such as those with diabetic retinopathy (DR). We propose a novel methodology for software-based OCT-A motion correction achieved through serial acquisition, volumetric registration, and averaging. Motion artifacts are removed via a multi-step 3D registration process, and visibility is significantly enhanced through volumetric averaging. We demonstrate that this method permits clear 3D visualization of retinal pathologies and their surrounding features, 3D visualization of inner retinal capillary connections, as well as reliable visualization of the choriocapillaris layer.

Use of the Retinal Vascular Histology to Validate an Optical Coherence Tomography Angiography Technique.

Purpose: To determine the fidelity of optical coherence tomography angiography (OCTA) techniques by direct comparison of the retinal capillary network images obtained from the same region as imaged by OCTA and high-resolution confocal microscope. Method: Ten porcine eyes were perfused with red blood cells for OCTA image acquisition from the area centralis and then perfusion-fixed, and the vessels were labeled for confocal imaging. Two approaches involving post-processing of two-dimensional projection images and vessel tracking on three dimensional image stacks were used to obtain quantitative measurements. Data collected include vessel density, length of visible vessel track, count of visible branch points, vessel track depth, vessel diameter, angle of vessel descent, and angle of dive for comparison and analysis. Results: Comparing vascular images acquired from OCTA and confocal microscopy, we found (1) a good representation of the larger caliber retinal vessels, (2) an underrepresentation of retinal microvessels smaller than 10 µm and branch points in all four retinal vascular plexuses, particularly the intermediate capillary plexus, (3) reduced visibility associated with an increase in the angle of descent, (4) a tendency to loss visibility of vessel track at a branch point or during a sharp dive, and (5) a reduction in visibility with increase in retinal depth on OCTA images. Conclusions: Current OCTA techniques can visualize the retinal capillary network, but some types of capillaries cannot be detected by OCTA, particularly in the middle to deeper layers. Translational Relevance: The information indicates the limitation in clinical use and scopes for improvement in the current OCTA technologies.

Federated Learning for Microvasculature Segmentation and Diabetic Retinopathy Classification of OCT Data.

Purpose: To evaluate the performance of a federated learning framework for deep neural network-based retinal microvasculature segmentation and referable diabetic retinopathy (RDR) classification using OCT and OCT angiography (OCTA). Design: Retrospective analysis of clinical OCT and OCTA scans of control participants and patients with diabetes. Participants: The 153 OCTA en face images used for microvasculature segmentation were acquired from 4 OCT instruments with fields of view ranging from 2 × 2-mm to 6 × 6-mm. The 700 eyes used for RDR classification consisted of OCTA en face images and structural OCT projections acquired from 2 commercial OCT systems. Methods: OCT angiography images used for microvasculature segmentation were delineated manually and verified by retina experts. Diabetic retinopathy (DR) severity was evaluated by retinal specialists and was condensed into 2 classes: non-RDR and RDR. The federated learning configuration was demonstrated via simulation using 4 clients for microvasculature segmentation and was compared with other collaborative training methods. Subsequently, federated learning was applied over multiple institutions for RDR classification and was compared with models trained and tested on data from the same institution (internal models) and different institutions (external models). Main Outcome Measures: For microvasculature segmentation, we measured the accuracy and Dice similarity coefficient (DSC). For severity classification, we measured accuracy, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, balanced accuracy, F1 score, sensitivity, and specificity. Results: For both applications, federated learning achieved similar performance as internal models. Specifically, for microvasculature segmentation, the federated learning model achieved similar performance (mean DSC across all test sets, 0.793) as models trained on a fully centralized dataset (mean DSC, 0.807). For RDR classification, federated learning achieved a mean AUROC of 0.954 and 0.960; the internal models attained a mean AUROC of 0.956 and 0.973. Similar results are reflected in the other calculated evaluation metrics. Conclusions: Federated learning showed similar results to traditional deep learning in both applications of segmentation and classification, while maintaining data privacy. Evaluation metrics highlight the potential of collaborative learning for increasing domain diversity and the generalizability of models used for the classification of OCT data.