Automated detection of diabetic retinopathy in a fundus photographic screening population
Automated detection of diabetic retinopathy in a fundus photographic screening population
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DOI：
10.1167/iovs.02-0417
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发表时间：
2003-02-01
影响因子：
4.4
通讯作者：
Larsen, M
中科院分区：
文献类型：
--
作者：
Larsen, N;Godt, J;Larsen, M
目的.评估自动眼底摄影图像分析算法在高灵敏度和/或高特异性分离患有未经治疗的糖尿病视网膜病变的糖尿病患者与没有视网膜病变的糖尿病患者方面的性能。方法。这是一项回顾性横断面研究，纳入了137例接受常规摄影视网膜病变筛查的糖尿病患者的260只连续非光凝眼。采用散瞳60 °眼底照相，35 mm彩色透明胶片，单中心视野。常规分级是基于载玻片固定显微镜的目视检查。进行参考分级时特别强调实现高灵敏度。计算机辅助自动红色病变检测进行数字化的乳腺癌。当应用于包括任何一只眼睛未接受治疗的糖尿病视网膜病变患者和无视网膜病变的糖尿病患者的筛查人群时，自动病变检测正确识别了90.1%的视网膜病变患者和81.3%的无视网膜病变患者。用于方法学目的的逐眼分析表明，自动病变检测可适用于模拟各种视觉评价策略。当适应于高灵敏度时，自动化系统的灵敏度为93.1%，特异性为71.6%。当适应于高特异性时，自动系统显示灵敏度为76.4%，特异性为96.6%，与常规视觉分级密切匹配，灵敏度为76.4%，特异性为98.3%。可以利用可调整的优先级设置来进行来自糖尿病患者的筛选群体的眼底照片中的未治疗的糖尿病视网膜病变的自动检测，强调糖尿病视网膜病变的高灵敏度识别或视网膜病变不存在的高特异性识别，覆盖由人类观察者证明的视觉评估策略的相反极端。
PURPOSE. To evaluate the performance of an automated fundus photographic image-analysis algorithm in high-sensitivity and/or high-specificity segregation of patients with diabetes with untreated diabetic retinopathy from those without retinopathy.METHODS. This was a retrospective cross-sectional study of 260 consecutive nonphotocoagulated eyes in 137 diabetic patients attending routine photographic retinopathy screening. Mydriatic 60degrees fundus photography on 35-mm color transparency film was used, with a single fovea-centered field. Routine grading was based on visual examination of slide-mounted transparencies. Reference grading was performed with specific emphasis on achieving high sensitivity. Computer-assisted automated red lesion detection was performed on digitized transparencies.RESULTS. When applied in a screening population comprising patients with diabetes with untreated diabetic retinopathy in any eye and patients with diabetes without retinopathy, the automated lesion detection correctly identified 90.1% of patients with retinopathy and 81.3% of patients without retinopathy. A per-eye analysis for methodological purposes demonstrated that the automated lesion detection could be adapted to simulate various visual evaluation strategies. When adapted at high sensitivity, the automated system demonstrated sensitivity at 93.1% and specificity at 71.6%. When adapted at high specificity the automated system demonstrated sensitivity at 76.4% and specificity at 96.6%, closely matching routine visual grading at sensitivity 76.4% and specificity 98.3%.CONCLUSIONS. Automated detection of untreated diabetic retinopathy in fundus photographs from a screening population of patients with diabetes can be made with adjustable priority settings, emphasizing high-sensitivity identification of diabetic retinopathy or high-specificity identification of absence of retinopathy, covering opposing extremes of visual evaluation strategies demonstrated by human observers.