高度近视眼白内障术后视力预测的OCT深度学习算法表格数据

数据集获取（Figshare）

• 源站标题：Table_1_An Optical Coherence Tomography-Based Deep Learning Algorithm for Visual Acuity Prediction of Highly Myopic Eyes After Cataract Surgery.DOCX
• DOI：10.3389/fcell.2021.652848.s002
• 发布日期：2021-05-26
• 文件数：1 个
• 文件总体积：15.53 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_An_Optical_Coherence_Tomography-Based_Deep_Learning_Algorithm_for_Visual_Acuity_Prediction_of_Highly_Myopic_Eyes_After_Cataract_Surgery_DOCX/14677686

命令行下载

curl -L -o "Table_1_An Optical Coherence Tomography-Based Deep Learning Algorithm for Visual Acuity Prediction of Highly Myopic Eyes After Cataract Surgery.DOCX" "https://ndownloader.figshare.com/files/28188114"

文件清单

• Table_1_An Optical Coherence Tomography-Based Deep Learning Algorithm for Visual Acuity Prediction of Highly Myopic Eyes After Cataract Surgery.DOCX（15.53 KB）

文件总体积 15.53 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

BackgroundDue to complicated and variable fundus status of highly myopic eyes, their visual benefit from cataract surgery remains hard to be determined preoperatively. We therefore aimed to develop an optical coherence tomography (OCT)-based deep learning algorithms to predict the postoperative visual acuity of highly myopic eyes after cataract surgery.
Materials and MethodsThe internal dataset consisted of 1,415 highly myopic eyes having cataract surgeries in our hospital. Another external dataset consisted of 161 highly myopic eyes from Heping Eye Hospital. Preoperative macular OCT images were set as the only feature. The best corrected visual acuity (BCVA) at 4 weeks after surgery was set as the ground truth. Five different deep learning algorithms, namely ResNet-18, ResNet-34, ResNet-50, ResNet-101, and Inception-v3, were used to develop the model aiming at predicting the postoperative BCVA, and an ensemble learning was further developed. The model was further evaluated in the internal and external test datasets.
ResultsThe ensemble learning showed the lowest mean absolute error (MAE) of 0.1566 logMAR and the lowest root mean square error (RMSE) of 0.2433 logMAR in the validation dataset. Promising outcomes in the internal and external test datasets were revealed with MAEs of 0.1524 and 0.1602 logMAR and RMSEs of 0.2612 and 0.2020 logMAR, respectively. Considerable sensitivity and precision were achieved in the BCVA Promising prediction outcomes of postoperative BCVA were achieved by the novel OCT-trained deep learning model, which will be helpful for the surgical planning of highly myopic cataract patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcell.2021.652848.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_An_Optical_Coherence_Tomography-Based_Deep_Learning_Algorithm_for_Visual_Acuity_Prediction_of_Highly_Myopic_Eyes_After_Cataract_Surgery_DOCX/14677686）。