眼科医生对人工智能在眼科护理中可用性的接受度和看法（APPRAISE）跨国视角表1

数据集获取（Figshare）

• 源站标题：Table_1_Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective.DOCX
• DOI：10.3389/fmed.2022.875242.s002
• 发布日期：2022-10-13
• 文件数：1 个
• 文件总体积：23.11 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Acceptance_and_Perception_of_Artificial_Intelligence_Usability_in_Eye_Care_APPRAISE_for_Ophthalmologists_A_Multinational_Perspective_DOCX/21322344

命令行下载

curl -L -o "Table_1_Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective.DOCX" "https://ndownloader.figshare.com/files/37845348"

文件清单

• Table_1_Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective.DOCX（23.11 KB）

文件总体积 23.11 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

BackgroundMany artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians’’ perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract.
MethodsThis was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.
ResultsOne thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years’’ experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83.
ConclusionOphthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma,

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2022.875242.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Acceptance_and_Perception_of_Artificial_Intelligence_Usability_in_Eye_Care_APPRAISE_for_Ophthalmologists_A_Multinational_Perspective_DOCX/21322344）。