Walton O Bennett, Garoon Robert B, Weng Christina Y, Gross Jacob, Young Alex K, Camero Kathryn A, Jin Haoxing, Carvounis Petros E, Coffee Robert E, Chu Yvonne I
Harris Health System, Houston, Texas2Cullen Eye Institute, Baylor College of Medicine, Houston, Texas.
The University of Texas Medical School at Houston.
JAMA Ophthalmol. 2016 Feb;134(2):204-9. doi: 10.1001/jamaophthalmol.2015.5083.
Diabetic retinopathy is a leading cause of blindness, but its detrimental effects are preventable with early detection and treatment. Screening for diabetic retinopathy has the potential to increase the number of cases treated early, especially in populations with limited access to care.
To determine the efficacy of an automated algorithm in interpreting screening ophthalmoscopic photographs from patients with diabetes compared with a reading center interpretation.
DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort analysis of 15,015 patients with type 1 or 2 diabetes in the Harris Health System in Harris County, Texas, who had undergone a retinal screening examination and nonmydriatic fundus photography via the Intelligent Retinal Imaging System (IRIS) from June 2013 to April 2014 were included. The IRIS-based interpretations were compared with manual interpretation. The IRIS algorithm population statistics were calculated.
Sensitivity and false-negative rate of the IRIS computer-based algorithm compared with reading center interpretation of the same images.
A total of 15 015 consecutive patients (aged 18-98 years); mean 54.3 years with known type 1 or 2 diabetes underwent nonmydriatic fundus photography for a diabetic retinopathy screening examination. The sensitivity of the IRIS algorithm in detecting sight-threatening diabetic eye disease compared with the reading center interpretation was 66.4% (95% CI, 62.8%-69.9%) with a false-negative rate of 2%. The specificity was 72.8% (95% CI, 72.0%-73.5%). In a population where 15.8% of people with diabetes have sight-threatening diabetic eye disease, the IRIS algorithm positive predictive value was 10.8% (95% CI, 9.6%-11.9%) and the negative predictive value was 97.8% (95% CI, 96.8%-98.6%).
In this large urban setting, the IRIS computer algorithm-based screening program had a high sensitivity and a low false-negative rate, suggesting that it may be an effective alternative to conventional reading center image interpretation. The IRIS algorithm shows promise as a screening program, but algorithm refinement is needed to achieve better performance. Further studies of patient safety, cost-effectiveness, and widespread applications of this type of algorithm should be pursued to better understand the role of teleretinal imaging and automated analysis in the global health care system.
糖尿病视网膜病变是导致失明的主要原因，但通过早期检测和治疗，其有害影响是可以预防的。糖尿病视网膜病变筛查有可能增加早期治疗的病例数量，尤其是在获得医疗服务机会有限的人群中。
确定一种自动算法在解读糖尿病患者的筛查眼底照片方面与阅读中心解读相比的效果。
设计、地点和参与者：对得克萨斯州哈里斯县哈里斯健康系统中15015例1型或2型糖尿病患者进行回顾性队列分析，这些患者在2013年6月至2014年4月期间通过智能视网膜成像系统（IRIS）接受了视网膜筛查检查和非散瞳眼底摄影。将基于IRIS的解读与人工解读进行比较。计算了基于IRIS算法的人群统计数据。
将基于IRIS计算机算法与阅读中心对相同图像的解读相比的敏感性和假阴性率。
共有15015例连续患者（年龄18 - 98岁）；平均54.3岁，已知患有1型或2型糖尿病，接受了非散瞳眼底摄影以进行糖尿病视网膜病变筛查检查。与阅读中心解读相比，IRIS算法检测威胁视力的糖尿病眼病的敏感性为66.4%（95%CI，62.8% - 69.9%），假阴性率为2%。特异性为72.8%（95%CI，72.0% - 73.5%）。在15.8%的糖尿病患者患有威胁视力的糖尿病眼病的人群中，IRIS算法的阳性预测值为10.8%（95%CI，9.6% - 11.9%），阴性预测值为97.8%（95%CI，96.8% - 98.6%）。
在这个大型城市环境中，基于IRIS计算机算法的筛查项目具有高敏感性和低假阴性率，表明它可能是传统阅读中心图像解读的有效替代方法。IRIS算法作为一种筛查项目显示出前景，但需要对算法进行优化以实现更好的性能。应进一步研究患者安全性、成本效益以及此类算法的广泛应用，以更好地理解远程视网膜成像和自动分析在全球医疗保健系统中的作用。
