Predicting effectiveness of anti-VEGF injection through self-supervised learning in OCT images.

Anti-vascular endothelial growth factor (Anti-VEGF) therapy has become a standard way for choroidal neovascularization (CNV) and cystoid macular edema (CME) treatment. However, anti-VEGF injection is a long-term therapy with expensive cost and may be not effective for some patients. Therefore, predicting the effectiveness of anti-VEGF injection before the therapy is necessary. In this study, a new optical coherence tomography (OCT) images based self-supervised learning (OCT-SSL) model for predicting the effectiveness of anti-VEGF injection is developed. In OCT-SSL, we pre-train a deep encoder-decoder network through self-supervised learning to learn the general features using a public OCT image dataset. Then, model fine-tuning is performed on our own OCT dataset to learn the discriminative features to predict the effectiveness of anti-VEGF. Finally, classifier trained by the features from fine-tuned encoder as a feature extractor is built to predict the response. Experimental results on our private OCT dataset demonstrated that the proposed OCT-SSL can achieve an average accuracy, area under the curve (AUC), sensitivity and specificity of 0.93, 0.98, 0.94 and 0.91, respectively. Meanwhile, it is found that not only the lesion region but also the normal region in OCT image is related to the effectiveness of anti-VEGF.

HCDT: an integrated highly confident drug-target resource.

Drug-target association plays an important role in drug discovery, drug repositioning, drug synergy prediction, etc. Currently, a lot of drug-related databases, such as DrugBank and BindingDB, have emerged. However, these databases are separate, incomplete and non-uniform with different criteria. Here, we integrated eight drug-related databases; collected, filtered and supplemented drugs, target genes and experimentally validated (highly confident) associations and built a highly confident drug-target (HCDT: http://hainmu-biobigdata.com/hcdt) database. HCDT database includes 500 681 HCDT associations between 299 458 drugs and 5618 target genes. Compared to individual databases, HCDT database contains 1.1 to 254.2 times drugs, 1.8-5.5 times target genes and 1.4-27.7 times drug-target associations. It is normative, publicly available and easy for searching, browsing and downloading. Together with multi-omics data, it will be a good resource in analyzing the drug functional mechanism, mining drug-related biological pathways, predicting drug synergy, etc. Database URL: http://hainmu-biobigdata.com/hcdt.

Neural Network-Based Beam Pumper Model Optimization.

Beam pumper is the earliest and most popular rod pumper driven by surface dynamic transmission devices. Drawing on modern theories and methods of industrial model design, the model optimization of beam pumper could promote the diversity, serialization, standardization, generalization, precision balance, and energy reduction of beam pumper design. Therefore, this study tries to optimize the model of beam pumper based on a neural network. Specifically, the system efficiency of beam pumper was decomposed, the surface and downhole working efficiencies were analyzed, and the model optimization flow was explained for beam pumper. Then, a radial basis function (RBF) neural network was established and trained by the sample data on beam pumper model. Besides, the mapping between model parameters and the optimization objective (system efficiency) was constructed. Moreover, the authors summed up the model optimization contents of beam pumper and predicted the relevant parameters of model optimization. The results demonstrate the effectiveness of our model.

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

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.