目的:评估基于计算机视觉技术开发的眼底图像质量评估系统的准确性。方法:选取2016至2017年在“上海糖尿病眼病研究”中由上海市各社区卫生服务中心的工作人员采用免散瞳眼底照相机拍摄的787例2型糖尿病患者的2 397幅彩色眼底像图片作为测试数据集。患者年龄（69.65±19.09）岁，男性384例，女性403例。根据眼底图像预处理、成像质量评价、内容检测和评估结果输出4个模块开发眼底图像质量评估系统。将2 397幅彩色眼底像图片输入该系统自动进行图像质量评价和视盘、黄斑识别，并根据图像质量判断规则对图像进行合格与否的判断并分类。同时由12位专业眼底图片阅片医师对此数据集的图像质量进行人工分类，其中合格1 846幅，不合格551幅。将系统判断结果与人工判断结果进行比对分析。结果:眼底图像质量评估系统可对输入的彩色眼底像图片自动进行眼别和眼位识别，并进行图像质量评估，之后直观输出评估结果。每幅眼底图像评估时间<1 s。1 846幅人工判断为图像质量合格的图片，经系统判断亦为合格者1 788幅（96.86%）；551幅人工判断为不合格的图片经系统判断结果亦为不合格者550幅（99.82%）。图像质量不合格原因为图像过暗（62幅，11.27%）、图像过亮（51幅，9.27%）、黄斑区不清晰（59幅，10.73%）、黄斑视盘未见（36幅，6.54%）、未见眼底结构（125幅，22.73%）、图像模糊（175幅，31.82%）、图像有遮挡（42幅，7.64%）。系统评估与人工判断结果总体一致率为97.54%。结论:该眼底图像质量评估系统对眼底图像质量的评估结果与专业阅片医师判断结果一致性高，具有客观性。n （中华眼科杂志，2020，56：920-927）“,”Objective:To develop a fundus image quality assessment system based on computer vision technology and to verify its accuracy by comparing the results of artificial discrimination and using this system.Methods:The process of image evaluation was divided into four modules: fundus image preprocessing, fundus image quality evaluation, fundus image content detection and evaluation result output. The system was designed to automatically evaluate the image quality of each fundus image, identify the optic disc and macula, and judge whether the image was qualified or not according to the image quality discrimination rules. A total of 2 397 fundus images of 787 type 2 diabetes patients were selected as the test data set. The average age of the patients, including 384 males and 403 females, was (69.65±19.09) years old. The images were taken by the staff of community health service centers in Shanghai with a fundus camera. The fundus image quality assessment system was used to conduct quality control and classification of the data set. At the same time, 12 professional fundus picture readers were employed to conduct manual quality control and classification of this data set. The system quality control results and artificial quality discrimination results were compared and analyzed.Results:The fundus image quality assessment system automatically recognized left and right eyes and eye positions on the input fundus images. The quality control interface included four indicator lights, which respectively corresponded to the images with the optic disc or macula as the center of the left or right eye. Evaluation of each fundus image was completed within 1 second, and the results were automatically displayed on the user interface. The 2 397 fundus photos were identified manually as 1 846 qualified photos and 551 unqualified photos. Among the unqualified images, 62 (11.27%) were too dark, 51 (9.27%) were too bright, 59 (10.73%) were not clear in the macular area, 36 (6.54%) showed no macula or optic disc, 125 (22.73%) could not present the fundus structure, 175 (31.82%) were blurred, and 42 (7.64%) were blocked. The results of the system and manual assessment were consistent in 1 788 qualified images (96.86%) and 550 unqualified images (99.82%), with an overall consistency rate of 97.54%.Conclusion:The fundus image quality assessment system can achieve highly consistent results with the professional judgment of ophthalmologists and has the characteristics of objectivity. n (Chin J Ophthalmol, 2020, 56:920-927)