Chiang Michael F, Gelman Rony, Martinez-Perez M Elena, Du Yunling E, Casper Daniel S, Currie Leanne M, Shah Payal D, Starren Justin, Flynn John T
Department of Ophthalmology, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA.
J AAPOS. 2009 Oct;13(5):438-45. doi: 10.1016/j.jaapos.2009.08.011.
To review findings from the authors' published studies involving telemedicine and image analysis for retinopathy of prematurity (ROP) diagnosis.
Twenty-two ROP experts interpreted a set of 34 wide-angle retinal images for presence of plus disease. For each image, a reference standard diagnosis was defined from expert consensus. A computer-based system was used to measure individual and linear combinations of image parameters for arteries and veins: integrated curvature (IC), diameter, and tortuosity index (TI). Sensitivity, specificity, and receiver operating characteristic areas under the curve (AUC) for plus disease diagnosis were determined for each expert. Sensitivity and specificity curves were calculated for the computer-based system by varying the diagnostic cutoffs for arterial IC and venous diameter. Individual vessels from the original 34 images were identified with particular diagnostic cutoffs, and combined into composite wide-angle images using graphics editing software.
For plus disease diagnosis, expert sensitivity ranged from 0.308-1.000, specificity from 0.571-1.000, and AUC from 0.784 to 1.000. Among computer system parameters, one linear combination had AUC 0.967, which was greater than that of 18 of 22 (81.8%) experts. Composite computer-generated images were produced using the arterial IC and venous diameter values associated with 75% under-diagnosis of plus disease (ie, 25% sensitivity cutoff), 50% under-diagnosis of plus disease (ie, 50% sensitivity cutoff), and 25% under-diagnosis of plus disease (ie, 75% sensitivity cutoff).
Computer-based image analysis has the potential to diagnose severe ROP with comparable or better accuracy than experts, and could provide added value to telemedicine systems. Future quantitative definitions of plus disease might improve diagnostic objectivity.
回顾作者发表的涉及远程医疗和图像分析用于早产儿视网膜病变（ROP）诊断的研究结果。
22名ROP专家解读一组34张广角视网膜图像，判断有无加病。对于每张图像，通过专家共识确定参考标准诊断。使用基于计算机的系统测量动脉和静脉图像参数的个体值和线性组合：积分曲率（IC）、直径和迂曲指数（TI）。确定每位专家诊断加病的敏感性、特异性和曲线下面积（AUC）。通过改变动脉IC和静脉直径的诊断阈值，计算基于计算机的系统的敏感性和特异性曲线。从最初的34张图像中识别出具有特定诊断阈值的单个血管，并使用图形编辑软件将其组合成复合广角图像。
对于加病诊断，专家的敏感性范围为0.308 - 1.000，特异性范围为0.571 - 1.000，AUC范围为0.784至1.000。在计算机系统参数中，一种线性组合的AUC为0.967，高于22名专家中的18名（81.8%）。使用与加病75%漏诊（即25%敏感性阈值）、50%漏诊（即50%敏感性阈值）和25%漏诊（即75%敏感性阈值）相关的动脉IC和静脉直径值生成复合计算机生成图像。
基于计算机的图像分析有潜力以与专家相当或更高的准确性诊断重度ROP，并可为远程医疗系统提供附加价值。加病未来的定量定义可能会提高诊断的客观性。
