Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study

• Published in: PloS one 2022-02, Vol.17 (2), p.e0262111-e0262111
• Main Authors: Sodhi, Simrat K, Pereira, Austin, Oakley, Jonathan D, Golding, John, Trimboli, Carmelina, Russakoff, Daniel B, Choudhry, Netan
• Format: Article
• Language: English
• Subjects: 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
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0262111

To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source optical coherence tomography (SS-OCT) and OCT-angiography (OCT-A) data. In this phase IV, retrospective, proof of concept, single center study, SS-OCT data from 17 previously treated nARMD eyes was used to assess retinal layer thicknesses, as well as quantify intraretinal fluid (IRF), subretinal fluid (SRF), and serous pigment epithelium detachments (PEDs) using a novel deep learning-based, macular fluid segmentation algorithm. Baseline OCT and OCT-A morphological features and fluid measurements were correlated using the Pearson correlation coefficient (PCC) to changes in BCVA from baseline to week 52. Total retinal fluid (IRF, SRF and PED) volume at baseline had the strongest correlation to improvement in BCVA at month 12 (PCC = 0.652, p = 0.005). Fluid was subsequently sub-categorized into IRF, SRF and PED, with PED volume having the next highest correlation (PCC = 0.648, p = 0.005) to BCVA improvement. Average total retinal thickness in isolation demonstrated poor correlation (PCC = 0.334, p = 0.189). When two features, mean choroidal neovascular membranes (CNVM) size and total fluid volume, were combined and correlated with visual outcomes, the highest correlation increased to PCC = 0.695 (p = 0.002). In isolation, total fluid volume most closely correlates with change in BCVA values between baseline and week 52. In combination with complimentary information from OCT-A, an improvement in the linear correlation score was observed. Average total retinal thickness provided a lower correlation, and thus provides a lower predictive outcome than alternative metrics assessed. Clinically, a machine-learning approach to analyzing fluid metrics in combination with lesion size may provide an advantage in personalizing therapy and predicting BCVA outcomes at week 52.