数据表1：计算机辅助系统提高内镜医师检测结直肠息肉性能的多中心随机对照试验

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_A computer-aided system improves the performance of endoscopists in detecting colorectal polyps: a multi-center, randomized controlled trial.docx
• DOI：10.3389/fmed.2023.1341259.s001
• 发布日期：2024-01-24
• 文件数：1 个
• 文件总体积：16.88 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_computer-aided_system_improves_the_performance_of_endoscopists_in_detecting_colorectal_polyps_a_multi-center_randomized_controlled_trial_docx/25052399

命令行下载

curl -L -o "Data_Sheet_1_A computer-aided system improves the performance of endoscopists in detecting colorectal polyps: a multi-center, randomized controlled tr.docx" "https://ndownloader.figshare.com/files/44207450"

文件清单

• Data_Sheet_1_A computer-aided system improves the performance of endoscopists in detecting colorectal polyps: a multi-center, randomized controlled tr.docx（16.88 KB）

文件总体积 16.88 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

BackgroundUp to 45.9% of polyps are missed during colonoscopy, which is the major cause of post-colonoscopy colorectal cancer (CRC). Computer-aided detection (CADe) techniques based on deep learning might improve endoscopists’ performance in detecting polyps. We aimed to evaluate the effectiveness of the CADe system in assisting endoscopists in a real-world clinical setting.
MethodsThe CADe system was trained to detect colorectal polyps, recognize the ileocecal region, and monitor the speed of withdrawal during colonoscopy in real-time. Between 17 January 2021 and 16 July 2021. We recruited consecutive patients aged 18–75 years from three centers in China. We randomized patients in 1:1 groups to either colonoscopy with the CADe system or unassisted (control). The primary outcomes were the sensitivity and specificity of the endoscopists. We used subgroup analysis to examine the polyp detection rate (PDR) and the miss detection rate of endoscopists.
ResultsA total of 1293 patients were included. The sensitivity of the endoscopists in the experimental group was significantly higher than that of the control group (84.97 vs. 72.07%, p The CADe system can potentially improve the sensitivity of endoscopists in detecting polyps, reduce the missed detection of polyps in colonoscopy, and reduce the risk of CRC.
RegistrationThis clinical trial was registered with the Chinese Clinical Trial Registry (Trial Registration Number: ChiCTR2100041988).
Clinical trial registrationwebsite www.chictr.org.cn, identifier ChiCTR2100041988.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2023.1341259.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_A_computer-aided_system_improves_the_performance_of_endoscopists_in_detecting_colorectal_polyps_a_multi-center_randomized_controlled_trial_docx/25052399）。