Use of a Convolutional Neural Network to Predict the Response of Diabetic Macular Edema to Intravitreal Anti-VEGF
George Magrath1, Joseph Luvisi2, Daniel Russakoff3
1From the Department of Ophthalmology (G.M., J.L., E.S., J.B., A.L.), Storm Eye Institute, Medical University of South Carolina, Charleston, South Carolina; Opus Genetics (G.M., A.J., S.T.), Durham, North Carolina.
Purpose:
To utilize a convolutional neural network (CNN) to predict the response of treatment-naïve diabetic macular edema (DME) to a single injection of anti-vascular endothelial growth factor (anti-VEGF) with data from optical coherence tomography (OCT).
Design:
Retrospective study performed via chart review.
Methods:
Setting: This was a single-center study performed at the Storm Eye Institute, Medical University of South Carolina.
Patient Population:
Patients with a new diagnosis of DME who underwent intravitreal (IVT) anti-VEGF injections were eligible for inclusion, provided they had a baseline OCT scan at the time of diagnosis and a 1-month follow-up OCT scan after the first anti-VEGF injection. Exclusion criteria included prior treatment with anti-VEGF, lack of required OCT scans, coexistent macular degeneration, and macular edema due to other retinal diseases. Seventy-three (73) eyes from 53 patients were included.
Intervention:
The OCT scan from the baseline visit was compared to the follow-up OCT scan approximately 1 month after the first anti-VEGF injection to determine change in central subfield thickness (delta CST). The delta CST was fed into the CNN as a label to train the system to predict treatment response from only the baseline OCT scan.
Main Outcome Measure:
CNN prediction of treatment response to anti-VEGF. Treatment response was defined as a CST reduction of 10 µm or more.
Results:
Based on delta CST from 2 OCT scans, 57 eyes were responders and 16 eyes were non-responders to the initial anti-VEGF injection. Analyzing only the baseline OCT scan for each eye, the trained CNN demonstrated an area under the curve (AUC) of 0.81. At the reported operating point, the CNN correctly identified 45 of the 57 responder eyes (i.e., recall of 78.9%) and 11 of the 16 non-responder eyes (i.e., specificity of 68.8%).
Conclusions:
The results of this study demonstrate the potential of a CNN to predict the response of treatment-naïve DME to a single injection of anti-VEGF therapy. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.
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