Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning

PURPOSE: To apply a deep learning algorithm for automated, objective, and comprehensive quantification of optical coherence tomography (OCT) scans to a large real-world dataset of eyes with neovascular age-related macular degeneration (AMD), and make the raw segmentation output data openly available for further research. DESIGN: Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database. PARTICIPANTS: 2473 first-treated eyes and another 493 second-treated eyes that commenced therapy for neovascular AMD between June 2012 and June 2017. METHODS: A deep learning algorithm was used to segment all baseline OCT scans. Volumes were calculated for segmented features such as neurosensory retina (NSR), drusen, intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperreflective material (SHRM), retinal pigment epithelium (RPE), hyperreflective foci (HRF), fibrovascular pigment epithelium detachment (fvPED), and serous PED (sPED). Analyses included comparisons between first and second eyes, by visual acuity (VA) and by race/ethnicity, and correlations between volumes. MAIN OUTCOME MEASURES: Volumes of segmented features (mm3), central subfield thickness (CST) (μm). RESULTS: In first-treated eyes, the majority had both IRF and SRF (54.7%). First-treated eyes had greater volumes for all segmented tissues, with the exception of drusen, which was greater in second-treated eyes. In first-treated eyes, older age was associated with lower volumes for RPE, SRF, NSR and sPED; in second-treated eyes, older age was associated with lower volumes of NSR, RPE, sPED, fvPED and SRF. Eyes from black individuals had higher SRF, RPE and serous PED volumes, compared with other ethnic groups. Greater volumes of the vast majority of features were associated with worse VA. CONCLUSION: We report the results of large scale automated quantification of a novel range of baseline features in neovascular AMD. Major differences between first and second-treated eyes, with increasing age, and between ethnicities are highlighted. In the coming years, enhanced, automated OCT segmentation may assist personalization of real-world care, and the detection of novel structure-function correlations. These data will be made publicly available for replication and future investigation by the AMD research community

Spectral-Domain Optical Coherence Tomography for Macular Edema

Optical coherence tomography (OCT) is a rapid noncontact method that allows in vivo imaging of the retina and it has become an important component in clinical practice. OCT is a useful ancillary tool for assessing retinal diseases because of its ability to provide cross-sectional retinal images and quantitatively analyze retinal morphology. the introduction of spectral-domain OCT provided major improvements in image acquisition speed and image resolution. Future studies will address how these major technologic advances will impact the use of OCT in research and clinical practice.Universidade Federal de São Paulo, Dept Ophthalmol, BR-06023062 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Ophthalmol, BR-06023062 São Paulo, BrazilWeb of Scienc

New Landmarks, Signs, and Findings in Optical Coherence Tomography

Spectral domain optical coherence tomography (SD-OCT) is a common useful noninvasive imaging instrument which is used for the diagnosis and follow-up of macular disorders. The clinical findings by OCT in these pathologies are well known. Currently, due to the development of this technology and its wide use, new OCT findings have been reported in the literature. The aim of this chapter is to describe new pathological or abnormal signs and findings in SD-OCT, including hyperreflective spots or dots, flyer saucer sign, outer retinal tubulations, dipping sign, focal choroidal excavation, outer retina-choroid complex splitting, foveal pseudocyst, brush border pattern, dome-shaped macula, pearl necklace sign, choroidal macrovessel, cystoid foveal degeneration, and disorganization of the retinal inner layers (DRIL)

Functional outcomes after multiple treatments with ranibizumab in neovascular age-related macular degeneration beyond visual acuity

Purpose: To evaluate neuroretinal function and anatomical outcomes in patients with neovascular age-related macular degeneration (AMD) after three treatments with ranibizumab.\ud \ud Design: Observational case reports.\ud \ud Methods: We investigated visual function in three patients, one female (80 years) and two male (77 and 74 years) with neovascular AMD. Twenty healthy participants served as control group. We measured visual acuity (Bailey-Lovie charts), contrast sensitivity (Pelli-Robson) and neuroretinal function using the multifocal electroretinogram (mfERG). Central macular thickness was evaluated using optical coherence tomography (OCT). Main outcome measures were central and peripheral mfERG peak to trough (N1P1) response density amplitudes and peak (P1) implicit times. All tests were performed before the first treatment (baseline) and after each of the three treatments with intravitreal 0.3 mg ranibizumab.\ud \ud Results: Visual acuity and contrast sensitivity remained stable or improved. Central macular thickness decreased after three treatments in all three patients. We found no significant change in central and peripheral neuroretinal function in the AMD patients between pre- and posttreatments 2 and 3. Although the mfERG amplitudes in the AMD patients were not significantly reduced compared with the age-similar group at baseline, there was a statistically significant reduction in central and peripheral mfERG amplitudes after three treatments.\ud \ud Conclusion: Anatomical outcomes and central visual function improved or remained stable in the three AMD patients in concordance with past reports. Further investigations of possible adverse effects of ranibizumab on the central and peripheral neuroretina in large prospective clinical trials are suggested

Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning

PURPOSE: To evaluate the predictive utility of quantitative imaging biomarkers, acquired automatically from optical coherence tomography (OCT) scans, of cross-sectional and future visual outcomes of patients with neovascular age-related macular degeneration (AMD) starting anti-vascular endothelial growth factor (VEGF) therapy. DESIGN: Retrospective cohort study. PARTICIPANTS: Treatment-naïve, first-treated eyes of patients with neovascular AMD between 2007 and 2017 at Moorfields Eye Hospital (a large, UK single-centre) undergoing anti-VEGF therapy METHODS: Automatic segmentation was carried out by applying a deep learning segmentation algorithm to 137,379 OCT scans from 6467 eyes of 3261 patients with neovascular AMD. After applying selection criteria 926 eyes of 926 patients were taken forward for analysis. MAIN OUTCOME MEASURES: Correlation coefficients (R2) and mean absolute error (MAE) between quantitative OCT (qOCT) parameters and cross-sectional visual-function. The predictive value of these parameters for short-term visual change i.e. incremental visual acuity [VA] resulting from an individual injection, as well as, VA at distant timepoints (up to 12 months post-baseline). RESULTS: VA at distant timepoints could be predicted: R2 0.80 (MAE 5.0 ETDRS letters) and R2 0.7 (MAE 7.2) post-injection 3 and at 12 months post-baseline (both p < 0.001), respectively. Best performing models included both baseline qOCT parameters and treatment-response. Furthermore, we present proof-of-principle evidence that the incremental change in VA from an injection can be predicted: R2 0.14 (MAE 5.6) for injection 2 and R2 0.11 (MAE 5.0) for injection 3 (both p < 0.001). CONCLUSIONS: Automatic segmentation enables rapid acquisition of quantitative and reproducible OCT biomarkers with potential to inform treatment decisions in the care of neovascular AMD. This furthers development of point-of-care decision-aid systems for personalized medicine

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally

Fluid as a critical biomarker in neovascular age-related macular degeneration management: literature review and consensus recommendations.

Current guidelines on the management of patients with neovascular age-related macular degeneration (nAMD) lack clear recommendations on the interpretation of fluid as seen on optical coherence tomography (OCT) imaging and the incorporation of this information into an ongoing disease treatment strategy. Our objective was to review current guidelines and scientific evidence on the role of fluid as a biomarker in the management of nAMD, and develop a clinically oriented, practical algorithm for diagnosis and management based on a consensus of expert European retinal specialists. PubMed was searched for articles published since 2006 relating to the role of fluid in nAMD. A total of 654 publications were screened for relevance and 66 publications were included for review. Of these, 14 were treatment guidelines, consensus statements and systematic reviews or meta-analyses, in which OCT was consistently recommended as an important tool in the initial diagnosis and ongoing management of nAMD. However, few guidelines distinguished between types of fluid when providing recommendations. A total of 52 publications reported primary evidence from clinical trials, studies, and chart reviews. Observations from these were sometimes inconsistent, but trends were observed with regard to features reported as being predictive of visual outcomes. Based on these findings, diagnostic recommendations and a treatment algorithm based on a treat-and-extend (T&E) regimen were developed. These provide guidance on the diagnosis of nAMD as well as a simple treatment pathway based on the T&E regimen, with treatment decisions made according to the observations of fluid as a critical biomarker for disease activity

Quantification of key retinal features in early and late age-related macular degeneration using deep learning

PURPOSE: To develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD). DESIGN: Development and validation of a deep-learning model for feature segmentation METHODS: Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2,712 B-scans. A deep neural network was trained with this data to perform voxel-level segmentation of the 13 most common abnormalities (features). For evaluation, 112 B-scans from 112 patients with a diagnosis of neovascular AMD were annotated by four independent observers. Main outcome measures were Dice score, intra-class correlation coefficient (ICC), and free-response receiver operating characteristic (FROC) curve. RESULTS: On 11 of the 13 features, the model obtained a mean Dice score of 0.63 ± 0.15, compared to 0.61 ± 0.17 for the observers. The mean ICC for the model was 0.66 ± 0.22, compared to 0.62 ± 0.21 for the observers. Two features were not evaluated quantitatively due to lack of data. FROC analysis demonstrated that the model scored similar or higher sensitivity per false positives compared to the observers. CONCLUSIONS: The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials

A Case Study

OK Publisher Copyright: © 2023 American Academy of OphthalmologyPurpose: To investigate intraretinal neovascularization and microvascular anomalies by correlating in vivo multimodal imaging with corresponding ex vivo histology in a single patient. Design: A case study comprising clinical imaging from a community-based practice, and histologic analysis at a university-based research laboratory (clinicopathologic correlation). Participants: A White woman in her 90s treated with numerous intravitreal anti-VEGF injections for bilateral type 3 macular neovascularization (MNV) secondary to age-related macular degeneration (AMD). Methods: Clinical imaging comprised serial infrared reflectance, eye-tracked spectral-domain OCT, OCT angiography, and fluorescein angiography. Eye tracking, applied to the 2 preserved donor eyes, enabled the correlation of clinical imaging signatures with high-resolution histology and transmission electron microscopy. Main Outcome Measures: Histologic/ultrastructural descriptions and diameters of vessels seen in clinical imaging. Results: Six vascular lesions were histologically confirmed (type 3 MNV, n = 3; deep retinal age-related microvascular anomalies [DRAMAs], n = 3). Pyramidal (n = 2) or tangled (n = 1) morphologies of type 3 MNV originated at the deep capillary plexus (DCP) and extended posteriorly to approach without penetrating persistent basal laminar deposit. They did not enter the subretinal pigment epithelium (RPE)–basal laminar space or cross the Bruch membrane. Choroidal contributions were not found. The neovascular complexes included pericytes and nonfenestrated endothelial cells, within a collagenous sheath covered by dysmorphic RPE cells. Deep retinal age-related microvascular anomaly lesions extended posteriorly from the DCP into the Henle fiber and the outer nuclear layers without evidence of atrophy, exudation, or anti-VEGF responsiveness. Two DRAMAs lacked collagenous sheaths. External and internal diameters of type 3 MNV and DRAMA vessels were larger than comparison vessels in the index eyes and in aged normal and intermediate AMD eyes. Conclusions: Type 3 MNV vessels reflect specializations of source capillaries and persist during anti-VEGF therapy. The collagenous sheath of type 3 MNV lesions may provide structural stabilization. If so, vascular characteristics may be useful in disease monitoring in addition to fluid and flow signal detection. Further investigation with longitudinal imaging before exudation onset will help determine if DRAMAs are part of the type 3 MNV progression sequence. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.publishersversionpublishe

Statistical Modelling of the Visual Impact of Subretinal Fluid and Associated Features

Introduction: The aim of this study was to develop a statistical model to determine the visual significance of subretinal fluid (SRF) in combination with other constructed optical coherence tomography (OCT) features in patients with wet age-related macular degeneration. Methods: The project used labelled data from 1211 OCTs of patients with neovascular macular degeneration (nAMD) attending the macular treatment centre of Manchester Royal Eye Hospital to build a statistical model to determine vision for any virtual, constructed OCT. A four-dimensional plot was created to represent the visual impact of SRF in OCTs in the context of the associated OCT characteristics of atrophy and subretinal hyperreflective material (SHRM). Results: The plot illustrates that at levels of SRF below 150 µm, the impact of SRF on vision is very low. Increasing the amount of fluid to 200 µm and beyond increases the impact on vision, but only if there is little atrophy or SHRM. Conclusions: This study suggests that levels of SRF up to around 150 µm thickness on OCT have minimal impact on vision. Greater levels of SRF have greater impact on vision, unless associated with significant amounts of atrophy or SHRM, when the additional effect of the SRF on vision remains low