Reliability of Retinal Pathology Quantification in Age-Related Macular Degeneration: Implications for Clinical Trials and Machine Learning Applications

TRANSLATIONAL VISION SCIENCE & TECHNOLOGY(2021)

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摘要
Purpose: To investigate the interreader agreement for grading of retinal alterations in age-related macular degeneration (AMD) using a reading center setting. Methods: In this cross-sectional case series, spectral-domain optical coherence tomography (OCT; Topcon 3D OCT, Tokyo, Japan) scans of 112 eyes of 112 patients with neovascular AMD (56 treatment naive, 56 after three anti-vascular endothelial growth factor injections) were analyzed by four independent readers. Imaging features specific for AMD were annotated using a novel custom-built annotation platform. Dice score, Bland Altman plots, coefficients of repeatability, coefficients of variation, and intraclass correlation coefficients were assessed. Results: Loss of ellipsoid zone, pigment epithelium detachment, subretinal fluid, and drusen were the most abundant features in our cohort. Subretinal fluid, intraretinal fluid, hypertransmission, descent of the outer plexiform layer, and pigment epithelium detachment showed highest interreader agreement, while detection and measures of loss of ellipsoid zone and retinal pigment epithelium were more variable. The agreement on the size and location of the respective annotation was more consistent throughout all features. Conclusions: The interreader agreement depended on the respective OCT-based feature. A selection of reliable features might provide suitable surrogate markers for disease progression and possible treatment effects focusing on different disease stages. Translational Relevance: This might give opportunities for a more time-and costeffective patient assessment and improved decision making as well as have implications for clinical trials and training machine learning algorithms.
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关键词
retina,AMD,optical coherence tomography,OCT,imaging,interreader,interrater,agreement,annotation,artificial intelligence,machine learning,deep learning
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