We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom-built OCT system based on an akinetic swept laser at 1322 nm with a lateral resolution of ∼13 μm and using split-spectrum amplitude decorrelation. Our data set consisted of 24 stitched angiograms of the full ear, with a size of approximately 8.2 × 8.2 mm, evenly distributed between healthy and diabetic mice. The deep learning classification algorithm uses the ResNet v2 convolutional neural network architecture and was trained on small patches extracted from the full ear angiograms. For individual patches, we obtained a cross-validated accuracy of 0.925 and an area under the receiver operating characteristic curve (ROC AUC) of 0.974. Averaging over multiple patches extracted from each ear resulted in the correct classification of all 24 ears.

中文翻译：

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深度学习通过光学相干断层扫描血管造影图像区分健康和糖尿病小鼠耳朵

我们训练了一种深度学习算法，以使用皮肤光学相干断层扫描 (OCT) 血管造影来区分健康和 2 型糖尿病小鼠。OCT 血管造影照片是使用定制的 OCT 系统获得的，该系统基于 1322 nm 的非运动扫描激光，横向分辨率为~13 μm，并使用分谱幅度去相关。我们的数据集由 24 张全耳缝合血管造影照片组成，大小约为 8.2 × 8.2 mm，均匀分布在健康小鼠和糖尿病小鼠之间。深度学习分类算法使用 ResNet v2 卷积神经网络架构，并在从全耳血管造影中提取的小块上进行训练。对于单个补丁，我们获得了 0.925 的交叉验证准确度和 0.974 的受试者工作特征曲线下面积 (ROC AUC)。