基于深度学习OCT辅助诊断湿性年龄相关性黄斑变性算法的应用

Objective: To investigate the application value of deep learning optical coherence tomography (OCT) on wet age-related macular degeneration (wAMD) assisted diagnosis. Methods: Weakly supervised deep learning algorithms was applied on the premise that only disease or not can be provided as a marker.The OCT image automatically assisted in the diagnosis of diseased areas of wAMD, and thermograms were applied to provide a basis for doctors to detect disease areas.Based on the deep learning of weak supervision, a new network algorithm structure was proposed for detecting disease area in ophthalmic OCT images.At the same time, thermograms were adopted to improve the accuracy of the lesion map, which is the location of the lesion area.This study followed the Declaration of Helsinki.This study protocol was approved by Ethic Committee of Ningbo Eye Hospital (No.2018-YJ05). Written informed consent was obtained from each subject before entering study cohort. Results: Resnet-based deep learning algorithm gave a diagnostic accuracy rate of 94.9% for the disease, which was much higher than that of AlexNet 85.3%, VGG 88.7%, and Google-Net 89.2%.The thermograms with different colors provided a more convenient auxiliary diagnosis basis for doctors. Conclusions: Compared with the original classification network, which needs disease area markers as empirical knowledge, deep learning algorithm model not only provides better results in the classification of retinal diseases, but also marks potential disease areas.The lesion area provides a basis for judging the area of the lesion for the diagnosis of wAMD.