Abstract
Purpose :
Optical coherence tomography (OCT) is the main diagnostic tool to detect and monitor progression of neovascular age related macular degeneration (nAMD). The purpose of this study is to predict anti-VEGF treatment requirements in nAMD using artificial intelligence (AI) based on OCT images for identifying fluid biomarkers in a real-world cohort.
Methods :
OCT (Spectralis, Heidelberg Engineering) data of treatment-naïve patients with nAMD from the Fight Retinal Blindness! in Zürich were processed at baseline, and the end of the loading dose (2 months after the first anti-VEGF injection), to predict subsequent one year treatment needs. First, intraretinal (IRF), subretinal fluid (SRF) and pigment epithelial detachment (PED) were segmented using a deep learning convolutional neural network (Vienna Fluid Monitor, RetInSight, Vienna, Austria). Second, a set of quantitative features from the segmented layers and fluid regions were computed across the three central subfields at 1mm, 3mm, and 6mm, to describe retinal pathomorphology both quantitatively and spatially. Finally, using the computed set of features, a predictive model of future treatment requirements was built using machine learning and was evaluated with a ten-fold patient-level cross-validation.
Results :
Two hundred and nine eyes from 164 patients were evaluated for a one year period following the loading dose. The treatment intervals ranged from 0 to 13 weeks. 100/209 eyes had lower median (≤7) and 109/209 eyes had an upper median (≥8) number of injections. The model identified the two groups (lower and upper median) based on number of injections with a mean accuracy of 0.74 (CI) area under the curve (AUC). The amount of SRF after the loading dose and at baseline in the central-3mm area were found to be the most important predictive features (Figure 1).
Conclusions :
We used AI to predict treatment requirements in nAMD by correlating fluid biomarkers with resulting therapeutical patterns based on OCT images. The potential of a personalized anti-VEGF therapy for minimizing the risk of undertreatment while improving resource management and avoiding overtreatment will have to be evaluated in respect to this current state-of-the-art concept in a prospective manner.
This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.