A Preliminary Study of Predicting Effectiveness of Anti-VEGF Injection Using OCT Images Based on Deep Learning.

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Abstract

Deep learning based radiomics have made great progress such as CNN based diagnosis and U-Net based segmentation. However, the prediction of drug effectiveness based on deep learning has fewer studies. Choroidal neovascularization (CNV) and cystoid macular edema (CME) are the diseases often leading to a sudden onset but progressive decline in central vision. And the curative treatment using anti-vascular endothelial growth factor (anti-VEGF) may not be effective for some patients. Therefore, the prediction of the effectiveness of anti-VEGF for patients is important. With the development of Convolutional Neural Networks (CNNs) coupled with transfer learning, medical image classifications have achieved great success. We used a method based on transfer learning to automatically predict the effectiveness of anti-VEGF by Optical Coherence tomography (OCT) images before giving medication. The method consists of image preprocessing, data augmentation and CNN-based transfer learning, the prediction AUC can be over 0.8. We also made a comparison study of using lesion region images and full OCT images on this task. Experiments shows that using the full OCT images can obtain better performance. Different deep neural networks such as AlexNet, VGG-16, GooLeNet and ResNet-50 were compared, and the modified ResNet-50 is more suitable for predicting the effectiveness of anti-VEGF.Clinical Relevance – This prediction model can give an estimation of whether anti-VEGF is effective for patients with CNV or CME, which can help ophthalmologists make treatment plan.