Wisely C Ellis, Richardson Alexander, Henao Ricardo, Robbins Cason B, Ma Justin P, Wang Dong, Johnson Kim G, Liu Andy J, Grewal Dilraj S, Fekrat Sharon
Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina.
iMIND Study Group, Duke University School of Medicine, Durham, North Carolina.
Ophthalmol Sci. 2023 Jun 25;4(1):100355. doi: 10.1016/j.xops.2023.100355. eCollection 2024 Jan-Feb.
To develop a machine learning tool capable of differentiating eyes of subjects with normal cognition from those with mild cognitive impairment (MCI) using OCT and OCT angiography (OCTA).
Evaluation of a diagnostic technology.
Subjects with normal cognition were compared to subjects with MCI.
A multimodal convolutional neural network (CNN) was built to predict likelihood of MCI from ganglion cell-inner plexiform layer (GC-IPL) thickness maps, OCTA images, and quantitative data including patient characteristics.
Area under the receiver operating characteristic curve (AUC) and summaries of the confusion matrix (sensitivity and specificity) were used as performance metrics for the prediction outputs of the CNN.
Images from 236 eyes of 129 cognitively normal subjects and 154 eyes of 80 MCI subjects were used for training, validating, and testing the CNN. When applied to the independent test set using inputs including GC-IPL thickness maps, OCTA images, and quantitative OCT and OCTA data, the AUC value for the CNN was 0.809 (95% confidence interval [CI]: 0.681-0.937). This model achieved a sensitivity of 79% and specificity of 83%. The AUC value for GC-IPL thickness maps alone was 0.681 (95% CI: 0.529-0.832), for OCTA images alone was 0.625 (95% CI: 0.466-0.784) and for both GC-IPL maps and OCTA images was 0.693 (95% CI: 0.543-0.843). Models using quantitative data alone were also tested, with a model using quantitative data derived from images, 0.960 (95% CI: 0.902-1.00), outperforming a model using demographic data alone, 0.580 (95% CI: 0.417-0.742).
This novel CNN was able to identify an MCI diagnosis using an independent test set comprised of OCT and OCTA images and quantitative data. The GC-IPL thickness maps provided more useful decision support than the OCTA images. The addition of quantitative data inputs also provided significant decision support to the CNN to identify individuals with MCI. Quantitative imaging metrics provided superior decision support than demographic data.
Proprietary or commercial disclosure may be found after the references.
开发一种机器学习工具，能够使用光学相干断层扫描（OCT）和光学相干断层扫描血管造影（OCTA）将认知正常受试者的眼睛与轻度认知障碍（MCI）受试者的眼睛区分开来。
诊断技术评估。
将认知正常的受试者与MCI受试者进行比较。
构建一个多模态卷积神经网络（CNN），根据神经节细胞-内丛状层（GC-IPL）厚度图、OCTA图像以及包括患者特征在内的定量数据来预测MCI的可能性。
受试者工作特征曲线下面积（AUC）以及混淆矩阵的汇总指标（敏感性和特异性）被用作CNN预测输出的性能指标。
来自129名认知正常受试者的236只眼睛和80名MCI受试者的154只眼睛的图像用于训练、验证和测试CNN。当使用包括GC-IPL厚度图、OCTA图像以及定量OCT和OCTA数据的输入应用于独立测试集时，CNN的AUC值为0.809（95%置信区间[CI]：0.681-0.937）。该模型的敏感性为79%，特异性为83%。单独GC-IPL厚度图的AUC值为0.681（95%CI：0.529-0.832），单独OCTA图像的AUC值为0.625（95%CI：0.466-0.784），GC-IPL图和OCTA图像两者的AUC值为0.693（95%CI：0.543-0.843）。还测试了仅使用定量数据的模型，使用从图像得出的定量数据的模型的AUC值为0.960（95%CI：0.902-1.00），优于仅使用人口统计学数据的模型，其AUC值为0.580（95%CI：0.417-0.742）。
这种新型CNN能够使用由OCT和OCTA图像以及定量数据组成的独立测试集来识别MCI诊断。GC-IPL厚度图比OCTA图像提供了更有用的决策支持。添加定量数据输入也为CNN识别MCI个体提供了重要的决策支持。定量成像指标比人口统计学数据提供了更好的决策支持。
专有或商业披露信息可在参考文献之后找到。
