10/3/2023 0 Comments Optical coherence tomography scanIn an inter-observer comparison between manual readers R1 and R2, the R 2 coefficient was 0.968 for IRF, 0.960 for SRF, and 0.906 for nPED, with Dice coefficients of 0.692, 0.660 and 0.784 for the same features. The deep learning–based algorithms had high accuracies for all fluid types between all models and readers: per B-scan IRF AUCs were 0.953, 0.932, 0.990, 0.942 for comparisons A1-R1, A1-R2, A2-R1 and A2-R2, respectively SRF AUCs were 0.984, 0.974, 0.987, 0.979 and nPED AUCs were 0.963, 0.969, 0.961 and 0.966. Area under the curve (AUC) values gauged detection performance, and quantification between readers and models was evaluated using Dice and correlation (R 2) coefficients. Two models, A1 and A2, were created based on gradings from two masked readers, R1 and R2. In this IRB-approved study, optical coherence tomography (OCT) data from 50 patients (50 eyes) with exudative nAMD were retrospectively analysed. To validate a deep learning algorithm for automated intraretinal fluid (IRF), subretinal fluid (SRF) and neovascular pigment epithelium detachment (nPED) segmentations in neovascular age-related macular degeneration (nAMD).
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