Leina Lunasco

Machine Learning-Based Automated Detection and Quantification of Geographic Atrophy and Hypertransmission Defects Using Spectral Domain Optical Coherence Tomography

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The current study describes the development and assessment of innovative, machine learning (ML)-based approaches for automated detection and pixel-accurate measurements of regions with geographic atrophy (GA) in late-stage age-related macular degeneration (AMD) using optical coherence tomography systems. 900 OCT volumes, 100266 B-scans, and en face OCT images from 341 non-exudative AMD patients with or without GA were included in this study from both Cirrus (Zeiss) and Spectralis (Heidelberg) OCT systems. B-scan and en face level ground truth GA masks were created on OCT B-scan where the segmented ellipsoid zone (EZ) line, retinal pigment epithelium (RPE) line, and bruchs membrane (BM) line overlapped. Two deep learning-based approaches, B-scan level and en face level, were trained. The OCT B-scan model had detection accuracy of 91% and GA area measurement accuracy of 94%. The en face OCT model had detection accuracy of 82% and GA area measurement accuracy of 96% with primary target...

Risk Classification for Progression to Subfoveal Geographic Atrophy in Dry Age-Related Macular Degeneration Using Machine Learning–Enabled Outer Retinal Feature Extraction

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Background and objective: To evaluate the utility of spectral-domain optical coherence tomography biomarkers to predict the development of subfoveal geographic atrophy (sfGA). Patients and methods: This was a retrospective cohort analysis including 137 individuals with dry age-related macular degeneration without sfGA with 5 years of follow-up. Multiple spectral-domain optical coherence tomography quantitative metrics were generated, including ellipsoid zone (EZ) integrity and subretinal pigment epithelium (sub-RPE) compartment features. Results: Reduced mean EZ-RPE central subfield thickness and increased sub-RPE compartment thickness were significantly different between sfGA convertors and nonconvertors at baseline in both 2-year and 5-year sfGA risk assessment. Longitudinal change assessment showed a significantly higher degradation of EZ integrity in sfGA convertors. The predictive performance of a machine learning classification model based on 5-year and 2-year risk conversion ...

Longitudinal Higher-Order OCT Assessment of Quantitative Fluid Dynamics and the Total Retinal Fluid Index in Neovascular AMD

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Purpose : The purpose of this study was to evaluate the feasibility of assessing quantitative longitudinal fluid dynamics and total retinal fluid indices (TRFIs) with higher-order optical coherence tomography (OCT) for neovascular age-related macular degeneration (nAMD). Methods : A post hoc image analysis study was performed using the phase II OSPREY clinical trial comparing brolucizumab and aflibercept in nAMD. Higher-order OCT analysis using a machine learning−enabled fluid feature extraction platform was used to segment intraretinal fluid (IRF) and subretinal fluid (SRF) volumetric components. TRFI, the proportion of fluid volume against total retinal volume, was calculated. Longitudinal fluid metrics were evaluated for the following groups: all subjects (i.e. treatment agnostic), brolucizumab, and aflibercept. Results : Mean IRF and SRF volumes were significantly reduced from baseline at each timepoint for all groups. Fluid feature extraction allowed high-resolution asses...

Aqueous Cytokine Expression and Higher-Order OCT Biomarkers: Assessment of the Anatomic-Biologic Bridge in the Imagine Dme Study

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Purpose To identify biomarkers for predicting response to anti-VEGF therapy in diabetic macular edema (DME) and evaluate any links between cytokine expression and OCT phenotype. Design IMAGINE DME is a post-hoc image analysis and cytokine expression assessment of the DAVE randomized clinical trial. Methods Subjects were categorized as anatomical Responders or Nonresponders,and within the Responder group as Rebounders and Nonrebounders based on quantitative, longitudinal optical coherence tomography (OCT) criteria. Retinal layer and fluid features were extracted using an OCT machine-learning augmented segmentation platform. Responders were further sub-classified by rapidity of response. Aqueous concentrations of 54 cytokines at multiple timepoints. Expression was compared between Responder groups and correlated with OCT imaging biomarkers. Results Of the 24 eyes studied, 79% were anatomical Responders with 38% Super Responders, 17% Early Responders, 25% Slow Responders. Twenty-one pe...


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