Noble Eye Care; Narayana Superspecialty Hospital, Gurugram, Haryana, India.
Division of Ophthalmology, Medanta-The Medicity, Gurugram, Haryana, India.
Indian J Ophthalmol. 2019 Jul;67(7):1089-1094. doi: 10.4103/ijo.IJO_1509_18.
To clinically validate a new automated glaucoma diagnosis software RIA-G.
A double-blinded study was conducted where 229 valid random fundus images were evaluated independently by RIA-G and three expert ophthalmologists. Optic nerve head parameters [vertical and horizontal cup-disc ratio (CDR) and neuroretinal rim (NRR) changes] were quantified. Disc damage likelihood scale (DDLS) staging and presence of glaucoma were noted. The software output was compared with consensus values of ophthalmologists.
Mean difference between the vertical CDR output by RIA-G and the ophthalmologists was - 0.004 ± 0.1. Good agreement and strong correlation existed between the two [interclass correlation coefficient (ICC) 0.79; r = 0.77, P < 0.005]. Mean difference for horizontal CDR was - 0.07 ± 0.13 with a moderate to strong agreement and correlation (ICC 0.48; r = 0.61, P < 0.05). Experts and RIA-G found a violation of the inferior-superior NRR in 47 and 54 images, respectively (Cohen's kappa = 0.56 ± 0.07). RIA-G accurately detected DDLS in 66.2% cases, while in 93.8% cases, output was within ± 1 stage (ICC 0.51). Sensitivity and specificity of RIA-G to diagnose glaucomatous neuropathy were 82.3% and 91.8%, respectively. Overall agreement between RIA-G and experts for glaucoma diagnosis was good (Cohen's kappa = 0.62 ± 0.07). Overall accuracy of RIA-G to detect glaucomatous neuropathy was 90.3%. A detection error rate of 5% was noted.
RIA-G showed good agreement with the experts and proved to be a reliable software for detecting glaucomatous optic neuropathy. The ability to quantify optic nerve head parameters from simple fundus photographs will prove particularly useful in glaucoma screening, where no direct patient-doctor contact is established.
临床验证一种新的自动化青光眼诊断软件 RIA-G。
进行了一项双盲研究,其中 229 张有效的随机眼底图像由 RIA-G 和三位专家眼科医生独立评估。视神经头参数[垂直和水平杯盘比(CDR)和神经视网膜边缘(NRR)变化]被量化。记录视盘损伤可能性量表(DDLS)分期和青光眼的存在。将软件输出与眼科医生的共识值进行比较。
RIA-G 输出的垂直 CDR 与眼科医生的测量值之间的平均差异为-0.004±0.1。两者之间存在良好的一致性和强相关性[组内相关系数(ICC)0.79;r=0.77,P<0.005]。水平 CDR 的平均差异为-0.07±0.13,具有中度到强的一致性和相关性(ICC 0.48;r=0.61,P<0.05)。专家和 RIA-G 分别在 47 张和 54 张图像中发现了下至上 NRR 的违反(Cohen's kappa=0.56±0.07)。RIA-G 准确地检测到 66.2%的病例中的 DDLS,而在 93.8%的病例中,输出在±1 级内(ICC 0.51)。RIA-G 诊断青光眼性神经病的敏感性和特异性分别为 82.3%和 91.8%。RIA-G 和专家对青光眼诊断的总体一致性良好(Cohen's kappa=0.62±0.07)。RIA-G 检测青光眼性神经病的总体准确性为 90.3%。检测错误率为 5%。
RIA-G 与专家具有良好的一致性,被证明是一种可靠的检测青光眼性视神经病变的软件。从简单的眼底照片中定量视神经头参数的能力将在青光眼筛查中特别有用,因为在这种筛查中,无法建立直接的医患接触。