Synopsis A deep-learning-based macular extrafoveal avascular area (EAA) on a 6×6 mm optical coherence tomography (OCT) angiogram is less dependent on the signal strength and shadow artefacts, providing better diagnostic accuracy for diabetic retinopathy (DR) severity than the commercial software measured extrafoveal vessel density (EVD). Aims To compare a deep-learning-based EAA to commercial output EVD in the diagnostic accuracy of determining DR severity levels from 6×6 mm OCT angiography (OCTA) scans. Methods The 6×6 mm macular OCTA scans were acquired on one eye of each participant with a spectral-domain OCTA system. After excluding the central 1 mm diameter circle, the EAA on superficial vascular complex was measured with a deep-learning-based algorithm, and the EVD was obtained with commercial software. Results The study included 34 healthy controls and 118 diabetic patients. EAA and EVD were highly correlated with DR severity (ρ=0.812 and −0.577, respectively, both p<0.001) and visual acuity (r=−0.357 and 0.420, respectively, both p<0.001). EAA had a significantly (p<0.001) higher correlation with DR severity than EVD. With the specificity at 95%, the sensitivities of EAA for differentiating diabetes mellitus (DM), DR and severe DR from control were 80.5%, 92.0% and 100.0%, respectively, significantly higher than those of EVD 11.9% (p=0.001), 13.6% (p<0.001) and 15.8% (p<0.001), respectively. EVD was significantly correlated with signal strength index (SSI) (r=0.607, p<0.001) and shadow area (r=−0.530, p<0.001), but EAA was not (r=−0.044, p=0.805 and r=−0.046, p=0.796, respectively). Adjustment of EVD with SSI and shadow area lowered sensitivities for detection of DM, DR and severe DR. Conclusion Macular EAA on 6×6 mm OCTA measured with a deep learning-based algorithm is less dependent on the signal strength and shadow artefacts, and provides better diagnostic accuracy for DR severity than EVD measured with the instrument-embedded software. Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplementary information. None.

中文翻译：

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基于深度学习的信号非依赖性评估糖尿病视网膜病变 6×6 mm 光学相干断层扫描血管造影黄斑无血管区域：与仪器嵌入式软件的比较

概要 6×6 mm 光学相干断层扫描 (OCT) 血管造影上基于深度学习的黄斑中心凹外无血管区域 (EAA) 对信号强度和阴影伪影的依赖程度较低，为糖尿病视网膜病变 (DR) 严重程度提供比商业软件测量中心凹外血管密度（EVD）。目的 比较基于深度学习的 EAA 与商业输出 EVD 在通过 6×6 mm OCT 血管造影 (OCTA) 扫描确定 DR 严重程度的诊断准确性方面。方法 使用谱域 OCTA 系统对每位参与者的一只眼睛进行 6×6 mm 黄斑 OCTA 扫描。排除中心直径为 1 mm 的圆圈后，使用基于深度学习的算法测量浅表血管复合体的 EAA，并使用商业软件获得 EVD。结果 该研究包括 34 名健康对照者和 118 名糖尿病患者。EAA 和 EVD 与 DR 严重程度（分别为 ρ=0.812 和 -0.577，均 p<0.001）和视力（分别为 r=-0.357 和 0.420，均 p<0.001）高度相关。与 EVD 相比，EAA 与 DR 严重程度的相关性显着 (p<0.001)。EAA 的特异性为 95%，区分糖尿病 (DM)、DR 和重度 DR 与对照的敏感性分别为 80.5%、92.0% 和 100.0%，显着高于 EVD 的 11.9% (p=0.001) , 13.6% (p<0.001) 和 15.8% (p<0.001), 分别。EVD 与信号强度指数 (SSI) (r=0.607, p<0.001) 和阴影面积 (r=−0.530, p<0.001) 显着相关，但 EAA 与 (r=−0.044, p=0.805 和 r= -0.046，p=0.796）。使用 SSI 和阴影区域调整 EVD 降低了检测 DM、DR 和严重 DR 的灵敏度。结论 使用基于深度学习的算法测量的 6×6 mm OCTA 上的黄斑 EAA 对信号强度和阴影伪影的依赖性较小，并且比使用仪器嵌入式软件测量的 EVD 提供更好的 DR 严重性诊断准确性。可根据合理要求提供数据。所有与研究相关的数据都包含在文章中或作为补充信息上传。没有任何。可根据合理要求提供数据。所有与研究相关的数据都包含在文章中或作为补充信息上传。没有任何。可根据合理要求提供数据。所有与研究相关的数据都包含在文章中或作为补充信息上传。没有任何。