Gelman Rony, Jiang Lei, Du Yunling E, Martinez-Perez M Elena, Flynn John T, Chiang Michael F
Department of Ophthalmology, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA.
J AAPOS. 2007 Dec;11(6):532-40. doi: 10.1016/j.jaapos.2007.09.005. Epub 2007 Oct 29.
To measure accuracy of plus disease diagnosis by recognized experts in retinopathy of prematurity (ROP), and to conduct a pilot study examining performance of a computer-based image analysis system, Retinal Image multiScale Analysis (RISA).
Twenty-two ROP experts independently interpreted a set of 34 wide-angle retinal images for presence of plus disease. A reference standard diagnosis based on expert consensus was defined for each image. Images were analyzed by the computer-based system using individual and linear combinations of system parameters for arterioles and venules: integrated curvature (IC), diameter, and tortuosity index (TI). Sensitivity, specificity, and receiver operating characteristic areas under the curve (AUC) for plus disease diagnosis compared with the reference standard were determined for each expert, as well as for the computer-based system.
Expert sensitivity ranged from 0.308 to 1.000, specificity ranged from 0.571 to 1.000, and AUC ranged from 0.784 to 1.000. Among individual computer system parameters, venular IC had highest AUC (0.853). Among all computer system parameters, the linear combination of arteriolar IC, arteriolar TI, venular IC, venular diameter, and venular TI had highest AUC (0.967), which was greater than that of 18 (81.8%) of 22 experts.
Accuracy of ROP experts for plus disease diagnosis is imperfect. A computer-based image analysis system has potential to diagnose plus disease with high accuracy. Further research involving RISA system parameter cut-off values from this study are required to fully validate performance of this computer-based system compared with that of human experts.
测量早产儿视网膜病变（ROP）领域公认专家对加性病变诊断的准确性，并开展一项初步研究，考察基于计算机的图像分析系统——视网膜图像多尺度分析（RISA）的性能。
22名ROP专家独立解读一组34张广角视网膜图像，判断是否存在加性病变。为每张图像定义基于专家共识的参考标准诊断。使用该计算机系统，针对小动脉和小静脉的系统参数（积分曲率[IC]、直径和迂曲指数[TI]）的个体值及其线性组合，对图像进行分析。将每位专家以及该计算机系统诊断加性病变的结果与参考标准进行比较，确定其敏感性、特异性和曲线下面积（AUC）。
专家的敏感性范围为0.308至1.000，特异性范围为0.571至1.000，AUC范围为0.784至1.000。在计算机系统的各个参数中，小静脉IC的AUC最高（0.853）。在计算机系统的所有参数中，小动脉IC、小动脉TI、小静脉IC、小静脉直径和小静脉TI的线性组合AUC最高（0.967），高于22名专家中的18名（81.8%）。
ROP专家对加性病变诊断的准确性并不理想。基于计算机的图像分析系统有潜力高精度诊断加性病变。需要进一步开展涉及本研究中RISA系统参数临界值的研究，以全面验证该计算机系统与人类专家相比的性能。
