基于深度神经网络的眼底图像测量青光眼患者视盘边缘丢失的长期比率。
Long-Term Rate of Optic Disc Rim Loss in Glaucoma Patients Measured From Optic Disc Photographs With a Deep Neural Network.
机构信息
Glaucoma Division, Stein Eye Institute, Los Angeles, CA, USA.
Department of Ophthalmology, Dong-A University College of Medicine, Busan, Republic of Korea.
出版信息
Transl Vis Sci Technol. 2024 Sep 3;13(9):9. doi: 10.1167/tvst.13.9.9.
PURPOSE
This study uses deep neural network-generated rim-to-disc area ratio (RADAR) measurements and the disc damage likelihood scale (DDLS) to measure the rate of optic disc rim loss in a large cohort of glaucoma patients.
METHODS
A deep neural network was used to calculate RADAR and DDLS for each optic disc photograph (ODP). Patient demographics, diagnosis, intraocular pressure (IOP), and mean deviation (MD) from perimetry were analyzed as risk factors for faster progression of RADAR. Receiver operating characteristic (ROC) curves were used to compare RADAR and DDLS in their utility to distinguish glaucoma from glaucoma suspect (GS) and for detecting glaucoma progression.
RESULTS
A total of 13,679 ODPs with evidence of glaucomatous optic nerve damage from 4106 eyes of 2407 patients with glaucoma or GS were included. Of these eyes, 3264 (79.5%) had a diagnosis of glaucoma, and 842 (20.5%) eyes were GS. Mean ± SD baseline RADAR of GS and glaucoma were 0.67 ± 0.13 and 0.57 ± 0.18, respectively (P < 0.001). Older age, greater IOP fluctuation, baseline MD, right eye, and diagnosis of secondary open-angle glaucoma were associated with slope of RADAR. The mean baseline DDLS of GS and glaucoma were 3.78 and 4.39, respectively. Both RADAR and DDLS showed a less steep slope in advanced glaucoma. In glaucoma, the change of RADAR and DDLS correlated with the corresponding change in MD. RADAR and DDLS had a similar ability to discriminate glaucoma from GS and detect disease progression. Area under the ROC curve of RADAR and DDLS was 0.658 and 0.648.
CONCLUSIONS
Automated calculation of RADAR and DDLS with a neural network can be used to evaluate the extent and long-term rate of optic disc rim loss and is further evidence of long-term nerve fiber loss in treated patients with glaucoma.
TRANSLATIONAL RELEVANCE
Our study provides a large clinic-based experience for RADAR and DDLS measurements in GS and glaucoma with a neural network.
目的
本研究使用深度神经网络生成的视盘边缘面积比（RADAR）测量值和盘损伤可能性评分（DDLS）来测量大量青光眼患者的视盘边缘损失率。
方法
使用深度神经网络计算每张视神经盘照片（ODP）的 RADAR 和 DDLS。分析患者的人口统计学数据、诊断、眼内压（IOP）和视野平均偏差（MD）等因素，作为 RADAR 进展更快的风险因素。采用受试者工作特征（ROC）曲线比较 RADAR 和 DDLS 在区分青光眼与疑似青光眼（GS）以及检测青光眼进展方面的效用。
结果
共纳入了来自 2407 例青光眼或 GS 患者的 4106 只眼中 13679 张有青光眼视神经损伤证据的 ODP。这些眼中，3264 只（79.5%）诊断为青光眼，842 只（20.5%）为 GS。GS 和青光眼的基线平均±标准差 RADAR 分别为 0.67±0.13 和 0.57±0.18（P＜0.001）。年龄较大、IOP 波动较大、基线 MD、右眼和继发开角型青光眼诊断与 RADAR 斜率相关。GS 和青光眼的基线平均 DDLS 分别为 3.78 和 4.39。高级别青光眼的 RADAR 和 DDLS 斜率均较平坦。在青光眼患者中，RADAR 和 DDLS 的变化与相应的 MD 变化相关。RADAR 和 DDLS 具有相似的鉴别 GS 和检测疾病进展的能力。RADAR 和 DDLS 的 ROC 曲线下面积分别为 0.658 和 0.648。
结论
使用神经网络自动计算 RADAR 和 DDLS 可用于评估视盘边缘损失的程度和长期速率，并进一步证明了治疗后青光眼患者长期神经纤维损失。
翻译
杨硕
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