Lee Samuel C, Lee Elisa T, Wang Yiming, Klein Ronald, Kingsley Ronald M, Warn Ann
School of Electrical and Computer Engineering, University of Oklahoma, Norman, USA.
Arch Ophthalmol. 2005 Jun;123(6):759-64. doi: 10.1001/archopht.123.6.759.
To propose methods for computer grading of the severity of 3 early lesions, namely, hemorrhages and microaneurysms, hard exudates, and cotton-wool spots, and classification of nonproliferative diabetic retinopathy (NPDR) based on these 3 types of lesions.
Using a computer diagnostic system developed earlier, the number of each of the 3 early lesions and the size of each lesion in the standard photographs were determined. Computer classification criteria were developed for the levels of individual lesions and for NPDR. Evaluation of the criteria was performed using 430 fundus images with normal retinas or any degree of retinopathy and 361 fundus images with no retinopathy or the 3 early lesions only. The results were compared with those of the graders at the University of Wisconsin Ocular Epidemiology Reading Center and an ophthalmologist.
Agreement rates in the classification of NPDR between the computer system and human experts.
In determining the severity levels of individual lesions, the agreement rates between the computer system and the reading center were 82.6%, 82.6%, and 88.3% using the 430 images and 85.3%, 87.5%, and 93.1% using the 361 images, respectively, for hemorrhages and microaneurysms, hard exudates, and cotton-wool spots. When the "questionable" category was excluded, the corresponding agreement rates were 86.5%, 92.3%, and 91.0% using the 430 images and 89.7%, 96.3%, and 97.4% using the 361 images. In classifying NPDR, the agreement rates between the computer system and the ophthalmologist were 81.7% using the 430 images and 83.5% using the 361 images.
The proposed criteria for computer classification produced results that are comparable with those provided by human experts. With additional research, this computer system could become a useful clinical aid to physicians and a tool for screening, diagnosing, and classifying NPDR.
提出对三种早期病变(即出血和微动脉瘤、硬性渗出物以及棉絮斑)的严重程度进行计算机分级的方法,并基于这三种病变类型对非增殖性糖尿病视网膜病变(NPDR)进行分类。
使用早期开发的计算机诊断系统,确定标准照片中三种早期病变各自的数量以及每个病变的大小。制定了针对单个病变级别和NPDR的计算机分类标准。使用430张视网膜正常或有任何程度视网膜病变的眼底图像以及361张无视网膜病变或仅有三种早期病变的眼底图像对这些标准进行评估。将结果与威斯康星大学眼科流行病学阅读中心的分级人员以及一名眼科医生的结果进行比较。
计算机系统与人类专家在NPDR分类方面的一致率。
在确定单个病变的严重程度级别时,对于出血和微动脉瘤、硬性渗出物以及棉絮斑,使用430张图像时计算机系统与阅读中心的一致率分别为82.6%、82.6%和88.3%,使用361张图像时分别为85.3%、87.5%和93.1%。当排除“可疑”类别时,使用430张图像时相应的一致率为86.5%、92.3%和91.0%,使用361张图像时为89.7%、96.3%和97.4%。在对NPDR进行分类时,使用430张图像时计算机系统与眼科医生的一致率为81.7%,使用361张图像时为83.5%。
所提出的计算机分类标准产生的结果与人类专家提供的结果相当。通过进一步研究,该计算机系统可成为医生有用的临床辅助工具以及筛查、诊断和分类NPDR的工具。