基于深层卷积神经网络的眼科B型超声图像玻璃体视网膜疾病辅助诊断系统应用评估  

Evaluation of an aided diagnosis system for vitreous and retinal diseases by analyzing B-scan ultrasound images based on deep convolutional neural network

作　　者：于薏 周奕文 陈弟 胡珊[3] 杨燕宁[1] Yu Yi;Zhou Yiwen;Chen Di;Hu Shan;Yang Yanning(Department of Ophthalmology,Renmin Hospital of Wuhan University,Wuhan 430060,China;Department of Gastroenterology,Renmin Hospital of Wuhan University,Wuhan 430060,China;School of Resource and Environmental Science,Wuhan University,Wuhan 430060,China)

机构地区：[1]武汉大学人民医院眼科中心,430060 [2]武汉大学人民医院消化内镜中心,430060 [3]武汉大学资源与环境学院,430060

出　　处：《中华实验眼科杂志》2021年第9期792-797,共6页Chinese Journal Of Experimental Ophthalmology

基　　金：国家自然科学基金项目(81770899);湖北省重点研发计划项目(2020BCB055)。

摘　　要：目的探讨一种基于深层卷积神经网络的眼科B型超声图像诊断系统的临床应用价值。方法收集2018年1月至2020年10月于武汉大学人民医院眼科中心进行眼科B型超声检查的1278例受试者3600张B型超声图像,以此构建图像数据集,由3位资深专业眼科医师对图像进行标记。将数据集分为训练集2812张图像和测试集788张图像,采用深度学习算法构建诊断模型,检测模型识别视网膜脱离(RD)、玻璃体积血(VH)及玻璃体后脱离(PVD)的准确性。选取120张独立于数据库的B型超声图像,由3位高年资眼科超声医生进行评估并记录评估时间,并与模型评估结果进行对比分析。另选取8位低年资临床医生模型辅助前后分别对独立于数据库的另外150张眼科B型超声图像进行评估,对2次评估结果进行差异分析以评估模型辅助效果。结果本诊断模型识别正常眼、RD、VH、PVD以及其他疾病的准确度分别为0.954、0.909、0.881、0.990和0.920。人机对比中,模型识别各类眼底疾病的准确度与高年资医师相近,评估图像的时间约为高年资医生的1/2。经模型辅助后,8位低年资医师诊断准确度均有显著提升(P<0.01)。结论该智能评估模型诊断RD、VH、PVD的准确度较高,并能提高临床诊断效率,较好地辅助临床医生进行评估。Objective To explore the clinical value of a diagnostic system of ophthalmic B-scan ultrasound images based on deep convolutional neural network.Methods A total of 3600 ophthalmic B-scan ultrasound images of 1278 patients with an average age of(49.32±7.69)years at the Eye Center of Renmin Hospital of Wuhan University from January 2018 to October 2020 were collected to build an image database.These B-scan images were labeled by three ophthalmologists.The database was divided into the training dataset of 2812 images and the testing dataset of 788 images.The deep learning algorithm was used to build a diagnostic model,which can identify retinal detachment(RD),vitreous hemorrhage(VH)and posterior vitreous detachment(PVD),and the accuracy of the model was evaluated.Another 120 B-scan ultrasound images were collected for the human-computer comparison between the model and 3 senior ophthalmologists.Eight junior clinicians were selected to evaluate another 150 B-scan images with and without the assistance of the model,and the accuracy was analyzed to evaluate the effect of the model.This study adhered to the Declaration of Helsinki and the study protocol was approved by Renmin Hospital of Wuhan University(No.WDRY2020K-192).Results The accuracy of the model for identifying normal fundus,RD,VH,PVD and other diseases were 0.954,0.909,0.881,0.990 and 0.920,respectively.The accuracy of the model was similar to that of senior doctors,and the time the model used was almost half that of doctors.With the assistance of the model,the diagnostic accuracy of the 8 junior clinicians who participated in the training was significantly improved(P<0.01).Conclusions The accuracy of RD,VH and PVD identification of the intelligent evaluation system is good,and the system can improve the accuracy and efficiency of clinical examinations,and can better assist clinicians in clinical evaluation.

关 键 词：眼科B型超声 人工智能 深度学习 视网膜脱离 玻璃体积血 玻璃体后脱离 

分 类 号：R774.1[医药卫生—眼科]