Validation of an Automated Fluid Algorithm on Real-World Data of Neovascular Age-Related Macular Degeneration Over Five Years

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(VIBES, -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.

Validation of an Automated Artificial Intelligence Algorithm for the Quantification of Major OCT Parameters in Diabetic Macular Edema

“…OCT imaging has become the gold standard for the diagnosis and grading of DME. Current evidence shows that different OCT biomarkers (IRF, SRF, ELM/EZ integrity, and HRF) are particularly related to DME outcomes, even more than the central retinal thickness [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 25 , 26 , 32 , 33 ]. Although these OCT biomarkers have been correlated with both pre-treatment and post-treatment anatomic and functional outcomes (e.g., retinal thickness and visual acuity), they have different pathogenetic characteristics and, therefore, represent different aspects (phenotypes) of DME.…”

“…AI previous algorithms have been considered to follow changes of IRF and SRF volumes over time, which may aid clinicians to assess disease activity and treatment response [ 25 , 26 , 33 ]. Although it is incredibly difficult to make comparisons between different algorithms used in the different studies, our results about an AI quantification approach have shown excellent accuracy and reproducibility of this AI algorithm in DME.…”

“…The literature data suggest that AI may achieve high performance in detecting retinal fluid [ 23 , 24 ] and in assessing anatomic changes over the course of the disease [ 25 ]. Moreover, particularly in eyes with age-related macular degeneration (AMD), AI has demonstrated its capability to detect both qualitatively and quantitatively the presence of IRF and SRF in a real-world scenario [ 26 ].…”

A systematic evaluation of human expert agreement on optical coherence tomography biomarkers using multiple devices

“…While CST is still used as an anatomical outcome measure in clinical trials, it is neither a reliable indicator of disease activity nor does it show a meaningful correlation with visual function over time. [29][30][31][32][33] Pawloff et al applied a precision AI uid algorithm to more than 2400 eyes to assess the correlation of (three-dimensional) retinal uid volumes and (twodimensional) central retinal sub eld thickness, demonstrating a surprisingly low correlation of r=0.57 in nAMD. [32] With the increasingly important role of retinal imaging in clinical trials, there has been a growing trend towards centralizing decisions on patient eligibility and disease monitoring in RCs.…”

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

“…There was good agreement for both the segmentation and detection of lesions between clinicians and the network (dice scores ≥ 0.75 for all features). Two applications with validated automated DL segmentation algorithms are currently commercially available: RetinAI (Medical AG, Switzerland) and RetInSight (Vienna, Austria) [ 137 ].…”