WINROP模型在ROP筛查中的应用（葡萄牙早产儿队列）

数据集获取（Figshare）

• 源站标题：Application of the WINROP model in Retinopathy of Prematurity (ROP) screening in a Portuguese cohort of premature infants
• DOI：10.6084/m9.figshare.7942097.v1
• 发布日期：2019-04-03
• 文件数：6 个
• 文件总体积：37.89 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/Application_of_the_WINROP_model_in_Retinopathy_of_Prematurity_ROP_screening_in_a_Portuguese_cohort_of_premature_infants/7942097

命令行下载

curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/14785298"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/14785310"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/14785322"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/14785334"
curl -L -o "Table_5.xls" "https://ndownloader.figshare.com/files/14785346"
curl -L -o "Table_6.xls" "https://ndownloader.figshare.com/files/14785355"

文件清单

• Table_1.xls（5.63 KB）
• Table_2.xls（5.63 KB）
• Table_3.xls（5.63 KB）
• Table_4.xls（5.63 KB）
• Table_5.xls（5.63 KB）
• Table_6.xls（9.73 KB）

文件总体积 37.89 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

Abstract Objective: To evaluate the sensitivity and specificity of the WINROP algorithm for predicting retinopathy of prematurity (ROP) in a population of Portuguese preterm infants. Methods: Retrospective study of all newborns who underwent ROP screening at Hospital Universitário do Porto from January 2010 to May 2016. Gestational age (GA), birthweight and weekly postnatal weight measurements of the newborns, up to a postmenstrual age of 35-36 weeks, were entered in the online application of WINROP, which signals with an alarm the newborns who are at risk of developing ROP type 1, which requires treatment. Results: Of a total of 496 RN undergoing ROP screening, 20 were excluded from the study by incomplete records and 101 by GA ≥32 weeks. Of the newborns with a GA≥32 weeks, one developed ROP type 1 and was treated. Of the 375 newborns introduced in the WINROP application, 231 recorded an alarm signal. All the newborns with type 1 ROP were identified by the alarm signal. The WINROP algorithm presented a sensitivity and a negative predictive value of 100% and a specificity of 41%. The mean time between the alarm signal and the treatment was 10 weeks plus 6 days. Conclusion: The WINROP model was 100% sensitive in the detection of preterm infants requiring treatment for ROP. Besides its lower specificity, WINROP application can reduce substantially the number of exams in ROP screening. One of the limitations of the model is the exclusion of newborns with GA≥32 weeks.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.7942097.v1（source: https://scielo.figshare.com/articles/dataset/Application_of_the_WINROP_model_in_Retinopathy_of_Prematurity_ROP_screening_in_a_Portuguese_cohort_of_premature_infants/7942097）。