探索基于人工智能的临床决策支持系统在初级保健中检测皮肤黑色素瘤的可行性——一项混合方法研究

数据集获取（Figshare）

• 源站标题：Exploring the feasibility of an artificial intelligence based clinical decision support system for cutaneous melanoma detection in primary care – a mixed method study
• DOI：10.6084/m9.figshare.24592352.v1
• 发布日期：2023-11-20
• 文件数：4 个
• 文件总体积：168.74 KB
• 源站页面：https://tandf.figshare.com/articles/dataset/Exploring_the_feasibility_of_an_artificial_intelligence_based_clinical_decision_support_system_for_cutaneous_melanoma_detection_in_primary_care_a_mixed_method_study/24592352

命令行下载

curl -L -o "ipri_a_2283190_sm7771.docx" "https://ndownloader.figshare.com/files/43206317"
curl -L -o "ipri_a_2283190_sm7753.docx" "https://ndownloader.figshare.com/files/43206320"
curl -L -o "ipri_a_2283190_sm7752.docx" "https://ndownloader.figshare.com/files/43206323"
curl -L -o "ipri_a_2283190_sm7713.docx" "https://ndownloader.figshare.com/files/43206326"

文件清单

• ipri_a_2283190_sm7771.docx（98.99 KB）
• ipri_a_2283190_sm7753.docx（25.18 KB）
• ipri_a_2283190_sm7752.docx（20.31 KB）
• ipri_a_2283190_sm7713.docx（24.26 KB）

文件总体积 168.74 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

Objective: Skin examination to detect cutaneous melanomas is commonly performed in primary care. In recent years, clinical decision support systems (CDSS) based on artificial intelligence (AI) have been introduced within several diagnostic fields.
Setting: This study employs a variety of qualitative and quantitative methodologies to investigate the feasibility of an AI-based CDSS to detect cutaneous melanoma in primary care.
Subjects and Design: Fifteen primary care physicians (PCPs) underwent near-live simulations using the CDSS on a simulated patient, and subsequent individual semi-structured interviews were explored with a hybrid thematic analysis approach. Additionally, twenty-five PCPs performed a reader study (diagnostic assessment on the basis of image interpretation) of 18 dermoscopic images, both with and without help from AI, investigating the value of adding AI support to a PCPs decision. Perceived instrument usability was rated on the System Usability Scale (SUS).
Results: From the interviews, the importance of trust in the CDSS emerged as a central concern. Scientific evidence supporting sufficient diagnostic accuracy of the CDSS was expressed as an important factor that could increase trust. Access to AI decision support when evaluating dermoscopic images proved valuable as it formally increased the physician’s diagnostic accuracy. A mean SUS score of 84.8, corresponding to ‘good’ usability, was measured.
Conclusion: AI-based CDSS might play an important future role in cutaneous melanoma diagnostics, provided sufficient evidence of diagnostic accuracy and usability supporting its trustworthiness among the users.
Effective primary care is important for discovering cutaneous melanoma, the deadliest and an increasingly prevalent form of skin cancer.

‘Trust’, ‘usability and user experience’, and ‘the clinical context’ are the qualitative themes that emerged from the qualitative analysis. These areas need to be considered for the successful adoption of AI assisted decision support tools by PCPs.
The AI CDSS tool was rated by the PCPs at grade B (average 84.8) on the System Usability Scale (SUS), which is equivalent to ‘good’ usability.
A reader st

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.24592352.v1（source: https://tandf.figshare.com/articles/dataset/Exploring_the_feasibility_of_an_artificial_intelligence_based_clinical_decision_support_system_for_cutaneous_melanoma_detection_in_primary_care_a_mixed_method_study/24592352）。