Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning.
Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning.
复制标题
使用深度学习预测圆锥角膜进展和角膜交联的需求。
DOI：
10.3390/jcm10040844
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发表时间：
2021-02-18
影响因子：
3.9
通讯作者：
Tsubota K
中科院分区：
文献类型：
--
作者：
Kato N;Masumoto H;Tanabe M;Sakai C;Negishi K;Torii H;Tabuchi H;Tsubota K
我们的目的是利用深度学习（DL）预测圆锥角膜的进展和角膜交联（CXL）的需要。本研究采用Pentacam HR®（Oculus, Wetzlar, Germany）对158例圆锥角膜患者拍摄的274张角膜断层图像进行了检查。所有患者检查2次及以上，分为两组；进展组和非进展组。根据患者的年龄，对初次检查时的额角膜平面轴向图、厚测图以及这两种图的组合进行评估。用卷积神经网络对这些学习数据对象进行训练。90只眼睛有进展，184只眼睛无进展。轴向图、厚视图及其结合患者年龄的平均AUC值分别为0.783、0.784、0.814（95%可信区间分别为0.721 ~ 0.845（0.722 ~ 0.846）、0.755 ~ 0.872)，敏感性分别为87.8%、77.8%、77.8%（79.2 ~ 93.7）、67.8 ~ 85.9、67.8 ~ 85.9)，特异性分别为59.8%、65.8%、69.6%（52.3 ~ 66.9）、58.4 ~ 72.6、62.4 ~ 76.1）。利用DL神经网络模型，结合患者的年龄，可以在角膜断层扫描图上预测圆锥角膜的进展。
We aimed to predict keratoconus progression and the need for corneal crosslinking (CXL) using deep learning (DL). Two hundred and seventy-four corneal tomography images taken by Pentacam HR® (Oculus, Wetzlar, Germany) of 158 keratoconus patients were examined. All patients were examined two times or more, and divided into two groups; the progression group and the non-progression group. An axial map of the frontal corneal plane, a pachymetry map, and a combination of these two maps at the initial examination were assessed according to the patients’ age. Training with a convolutional neural network on these learning data objects was conducted. Ninety eyes showed progression and 184 eyes showed no progression. The axial map, the pachymetry map, and their combination combined with patients’ age showed mean AUC values of 0.783, 0.784, and 0.814 (95% confidence interval (0.721–0.845) (0.722–0.846), and (0.755–0.872), respectively), with sensitivities of 87.8%, 77.8%, and 77.8% ((79.2–93.7), (67.8–85.9), and (67.8–85.9)) and specificities of 59.8%, 65.8%, and 69.6% ((52.3–66.9), (58.4–72.6), and (62.4–76.1)), respectively. Using the proposed DL neural network model, keratoconus progression can be predicted on corneal tomography maps combined with patients’ age.
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