Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study - Sodhi, Simrat K.; Pereira, Austin; Oakley, Jonathan D.; Golding, John; Trimboli, Carmelina; Russakoff, Daniel B.; Choudhry, Netan (PloS one, 2022)
Keywords: Acuity; Adult; Age; Age related diseases; Algorithms; Angiography; Artificial intelligence; Biology and Life Sciences; Biomarkers; Blindness; Clinical outcomes; Clinical trials; Complications and side effects; Computer and Information Sciences; Correlation; Correlation coefficient; Correlation coefficients; Deep Learning; Diabetes; Diabetic retinopathy; Edema; Epithelium; Eye diseases; Health aspects; Humans; Image segmentation; Intravitreal Injections; Learning algorithms; Machine learning; Macular degeneration; Macular Degeneration - diagnosis; Macular Degeneration - diagnostic imaging; Macular Degeneration - drug therapy; Medicine and Health Sciences; Ophthalmology; Optical Coherence Tomography; Optical tomography; Patients; Physical Sciences; Prognosis; Proof of Concept Study; Receptors, Vascular Endothelial Growth Factor - therapeutic use; Recombinant Fusion Proteins - therapeutic use; Research and Analysis Methods; Retina; Retina - diagnostic imaging; Retina - physiology; Retinal Detachment - pathology; Retrospective Studies; Risk factors; Social Sciences; Statistical analysis; Subretinal Fluid - physiology; Technology application; Thickness; Tomography; Tomography, Optical Coherence; Vascular endothelial growth factor; Visual Acuity; Canada; Ontario Canada; San Francisco California; United States--US