Hasan Cetin

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...

OCT-Derived Radiomic Features Predict Anti–VEGF Response and Durability in Neovascular Age-Related Macular Degeneration

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No established biomarkers currently exist for therapeutic efficacy and durability of anti–VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability. Design Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy. Participants Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non–super responders (patients who did not achieve or maintain retinal fluid resolution). Methods A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were e...

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 ...

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