VALIDATION OF AN AUTOMATED FLUID ALGORITHM ON REAL-WORLD DATA OF NEOVASCULAR AGE-RELATED MACULAR DEGENERATION OVER FIVE YEARS. Issue 9 (September 2022)

• Record Type: Journal Article
• Authors: Gerendas, Bianca S.; Sadeghipour, Amir; Michl, Martin; Goldbach, Felix; Mylonas, Georgios; Gruber, Anastasiia; Alten, Thomas; Leingang, Oliver; Sacu, Stefan; Bogunovic, Hrvoje; Schmidt-Erfurth, Ursula
• Is Part Of: Retina. Volume 42:Issue 9(2022)
• Page Start: 1673
• Page End: 1682
• Publication Date: 2022-09
• Subjects: deep learning -- fluid monitoring -- neovascular AMD -- OCT -- real-world management; Retina -- Diseases -- Periodicals; Retinal Diseases; Vitreous Body; 617.735
• DOI: 10.1097/IAE.0000000000003557
• Languages: English
• ISSNs: 0275-004X

Abstract

Abstract : Supplemental Digital Content is Available in the Text. For validation in real-world data, a deep learning–based automated fluid algorithm was successfully applied to precisely quantify intraretinal and subretinal fluid volumes in optical coherence tomography images from clinical routine in patients with neovascular age-related macular degeneration over 5 years of treatment. Abstract : Background/Purpose: To apply an automated deep learning automated fluid algorithm on data from real-world management of patients with neovascular age-related macular degeneration for quantification of intraretinal/subretinal fluid volumes in optical coherence tomography images. Methods: Data from the Vienna Imaging Biomarker Eye Study (VIBES, 2007–2018) were analyzed. Databases were filtered for treatment-naive neovascular age-related macular degeneration with a baseline optical coherence tomography and at least one follow-up and 1, 127 eyes included. Visual acuity and optical coherence tomography at baseline, Months 1 to 3/Years 1 to 5, age, sex, and treatment number were included. Artificial intelligence and certified manual grading were compared in a subanalysis of 20%. Main outcome measures were fluid volumes. Results: Intraretinal/subretinal fluid volumes were maximum at baseline (intraretinal fluid: 21.5/76.6/107.1 nL; subretinal fluid 13.7/86/262.5 nL in the 1/3/6-mm area). Intraretinal fluid decreased to 5 nL at M1-M3 (1-mm) and increased to 11 nL (Y1) and 16 nL (Y5). Subretinal fluid decreased to a mean of 4 nL at M1-M3 (1-mm) and remained stable below 7 nL until Y5. Intraretinal fluid was the only variable that reflected VA change over time. Comparison with human expert readings confirmed an area under the curve of >0.9. Conclusion: The Vienna Fluid Monitor can precisely quantify fluid volumes in optical coherence tomography images from clinical routine over 5 years. Automated tools will introduce precision medicine based on fluid guidance into real-world management of exudative disease, improving clinical outcomes while saving resources.