Srinivasan Sangeetha, Shetty Sharan, Natarajan Viswanathan, Sharma Tarun, Raman Rajiv
Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India.
Department of Preventive Ophthalmology, Sankara Nethralaya, Chennai-600 006, Tamil Nadu, India.
PLoS One. 2016 Sep 23;11(9):e0163108. doi: 10.1371/journal.pone.0163108. eCollection 2016.
To develop a simplified algorithm to identify and refer diabetic retinopathy (DR) from single-field retinal images specifically for sight-threatening diabetic retinopathy for appropriate care (ii) to determine the agreement and diagnostic accuracy of the algorithm as a pilot study among optometrists versus "gold standard" (retinal specialist grading).
The severity of DR was scored based on colour photo using a colour coded algorithm, which included the lesions of DR and number of quadrants involved. A total of 99 participants underwent training followed by evaluation. Data of the 99 participants were analyzed. Fifty posterior pole 45 degree retinal images with all stages of DR were presented. Kappa scores (κ), areas under the receiver operating characteristic curves (AUCs), sensitivity and specificity were determined, with further comparison between working optometrists and optometry students.
Mean age of the participants was 22 years (range: 19-43 years), 87% being women. Participants correctly identified 91.5% images that required immediate referral (κ) = 0.696), 62.5% of images as requiring review after 6 months (κ = 0.462), and 51.2% of those requiring review after 1 year (κ = 0.532). The sensitivity and specificity of the optometrists were 91% and 78% for immediate referral, 62% and 84% for review after 6 months, and 51% and 95% for review after 1 year, respectively. The AUC was the highest (0.855) for immediate referral, second highest (0.824) for review after 1 year, and 0.727 for review after 6 months criteria. Optometry students performed better than the working optometrists for all grades of referral.
The diabetic retinopathy algorithm assessed in this work is a simple and a fairly accurate method for appropriate referral based on single-field 45 degree posterior pole retinal images.
开发一种简化算法，用于从单视野视网膜图像中识别并转诊糖尿病视网膜病变（DR），特别是针对威胁视力的糖尿病视网膜病变，以便提供适当的治疗；（ii）作为一项试点研究，确定该算法与验光师和“金标准”（视网膜专科医生分级）之间的一致性和诊断准确性。
使用颜色编码算法根据彩色照片对DR的严重程度进行评分，该算法包括DR的病变和受累象限数量。共有99名参与者接受培训，随后进行评估。对这99名参与者的数据进行分析。展示了50张具有各阶段DR的后极45度视网膜图像。确定了kappa评分（κ）、受试者操作特征曲线下面积（AUC）、敏感性和特异性，并对在职验光师和验光专业学生进行了进一步比较。
参与者的平均年龄为22岁（范围：19 - 43岁），87%为女性。参与者正确识别出91.5%需要立即转诊的图像（κ = 0.696），62.5%的图像需要在6个月后复查（κ = 0.462），以及51.2%需要在1年后复查的图像（κ = 0.532）。验光师对于立即转诊的敏感性和特异性分别为91%和78%，6个月后复查为62%和84%，1年后复查为51%和95%。立即转诊的AUC最高（0.855），1年后复查次之（0.824），6个月后复查标准的AUC为0.727。在所有转诊等级中，验光专业学生的表现优于在职验光师。
本研究中评估的糖尿病视网膜病变算法是一种基于单视野45度后极视网膜图像进行适当转诊的简单且相当准确的方法。
