OCT image quality guarantee method based on deep learning

Aspects of the present disclosure relate to systems, methods, and algorithms that train machine learning models or neural networks to classify OCT images. A neural network or machine learning model may receive an annotated OCT image indicating which portions of the OCT image are occluded and which portions are clear and a classification of whether the OCT image is clear or occluded. After training, the neural network can be used to classify one or more new OCT images. A user interface may be provided to output results of the classification and overview the analysis of the one or more OCT images. 本公开的各方面涉及训练机器学习模型或神经网络以对OCT图像进行分类的系统、方法和算法。神经网络或机器学习模型可以接收带注释的OCT图像，所述带注释的OCT图像指示OCT图像的哪些部分被遮挡而哪些部分是清晰的以及OCT图像是清晰的还是被遮挡的分类。训练后，神经网络可用于对一个或多个新的OCT图像进行分类。用户界面可被提供来输出分类的结果，并概述对一个或多个OCT图像的分析。