Lee S C, Lee E T, Kingsley R M, Wang Y, Russell D, Klein R, Warn A
School of Electrical and Computer Engineering, University of Oklahoma, 202 W Boyd, Norman, OK 73019, USA.
Arch Ophthalmol. 2001 Apr;119(4):509-15. doi: 10.1001/archopht.119.4.509.
To investigate whether a computer vision system is comparable with humans in detecting early retinal lesions of diabetic retinopathy using color fundus photographs.
A computer system has been developed using image processing and pattern recognition techniques to detect early lesions of diabetic retinopathy (hemorrhages and microaneurysms, hard exudates, and cotton-wool spots). Color fundus photographs obtained from American Indians in Oklahoma were used in developing and testing the system. A set of 369 color fundus slides were used to train the computer system using 3 diagnostic categories: lesions present, questionable, or absent (Y/Q/N). A different set of 428 slides were used to test and evaluate the system, and its diagnostic results were compared with those of 2 human experts-the grader at the University of Wisconsin Fundus Photograph Reading Center (Madison) and a general ophthalmologist. The experiments included comparisons using 3 (Y/Q/N) and 2 diagnostic categories (Y/N) (questionable cases excluded in the latter).
In the training phase, the agreement rates, sensitivity, and specificity in detecting the 3 lesions between the retinal specialist and the computer system were all above 90%. The kappa statistics were high (0.75-0.97), indicating excellent agreement between the specialist and the computer system. In the testing phase, the results obtained between the computer system and human experts were consistent with those of the training phase, and they were comparable with those between the human experts.
The performance of the computer vision system in diagnosing early retinal lesions was comparable with that of human experts. Therefore, this mobile, electronically easily accessible, and noninvasive computer system, could become a mass screening tool and a clinical aid in diagnosing early lesions of diabetic retinopathy.
使用彩色眼底照片研究计算机视觉系统在检测糖尿病视网膜病变早期视网膜病变方面是否与人类相当。
已开发出一种利用图像处理和模式识别技术检测糖尿病视网膜病变早期病变（出血和微动脉瘤、硬性渗出物以及棉絮斑）的计算机系统。从俄克拉荷马州的美国印第安人那里获取的彩色眼底照片被用于该系统的开发和测试。一组369张彩色眼底幻灯片用于使用3种诊断类别（病变存在、可疑或不存在，即Y/Q/N）训练计算机系统。另一组428张幻灯片用于测试和评估该系统，其诊断结果与两位人类专家的诊断结果进行比较，这两位专家分别是威斯康星大学眼底照片阅读中心（麦迪逊）的分级人员和一位普通眼科医生。实验包括使用3种（Y/Q/N）和2种诊断类别（Y/N）（在后一种情况中排除可疑病例）进行比较。
在训练阶段，视网膜专家与计算机系统在检测这3种病变方面的一致率、敏感性和特异性均高于90%。kappa统计值很高（0.75 - 0.97），表明专家与计算机系统之间具有极佳的一致性。在测试阶段，计算机系统与人类专家之间获得的结果与训练阶段一致，并且与人类专家之间的结果相当。
计算机视觉系统在诊断早期视网膜病变方面的表现与人类专家相当。因此，这种便于携带、电子方式易于获取且无创的计算机系统，可成为糖尿病视网膜病变早期病变的大规模筛查工具和临床辅助手段。
