现有文献工作比较数据集
包含合成ARDS-COVID-19病例的DICOM影像数据集，用于比较区块链医疗影像治理方案的性能。
基本信息
资源简介
现有文献工作比较数据集
【数据集背景】
本数据集源站标题为《Comparison of our work with existing literature.》。源站首次发布：2026-03-18。
【数据内容】
源站原始描述：Medical imaging plays a critical role in diagnosing and managing acute conditions such as Acute Respiratory Distress Syndrome (ARDS), particularly in intensive care settings. However, radiological data are often siloed across Picture Archiving and Communication Systems (PACS), with limited interoperability, traceability, and patient control. This paper proposes and validates a blockchain-enabled architecture that integrates radiological imaging data into a patient-controlled digital wallet (BioW…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table 1.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pone.0338897.t001。
【主题与分类】
源站学科分类：Space Science、Cell Biology、Environmental Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy。主题标签：validation scenario replicating、managing acute conditions、intensive care settings、controlled digital wallet、annotated imaging datasets、imaging data governance、clinical data remain、blocking unauthorized access、architecture supports fair、high clinician satisfaction。
【适用方向】
影像类数据可用于医学图像分析模型（检测、分割、分类）的训练与评测，也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/_p_Comparison_of_our_work_with_existing_literature_p_/31806064
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Esperança, Marco; Galvão, Tiago; Freitas, Diogo; Ferreira, Joao C.; Cysneiros, Ana; Bento, Luís (2026). Comparison of our work with existing literature.. PLOS ONE. Dataset
下载信息
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需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
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公开数据集受托下载与技术交付服务。
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暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：<p>Comparison of our work with existing literature.</p>
- DOI：10.1371/journal.pone.0338897.t001
- 发布日期：2026-03-18
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/_p_Comparison_of_our_work_with_existing_literature_p_/31806064
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/62929327"
文件清单
Table 1.xls（9.73 KB）
文件总体积 9.73 KB（源站 API 实测）。
数据说明
Medical imaging plays a critical role in diagnosing and managing acute conditions such as Acute Respiratory Distress Syndrome (ARDS), particularly in intensive care settings. However, radiological data are often siloed across Picture Archiving and Communication Systems (PACS), with limited interoperability, traceability, and patient control. This paper proposes and validates a blockchain-enabled architecture that integrates radiological imaging data into a patient-controlled digital wallet (BioWallet) ecosystem. The system combines verifiable credentials, decentralized identifiers (DIDs), and a FHIR-compliant backend to ensure secure, auditable, and standards-based access to DICOM images and associated metadata. The ledger stores only consent and audit hashes, while clinical data remain off-chain and correctable within FHIR/EHR systems, ensuring auditability without hindering rectification. A validation scenario replicating an ICU emergency was conducted using synthetic ARDS-COVID19 cases to assess latency, consent enforceability, and user experience. Results showed a 67% reduction in image access time compared to traditional systems, 100% success in blocking unauthorized access, and high clinician satisfaction. The architecture supports FAIR-compliant reuse of annotated imaging datasets, enhances transparency in image-driven research, and aligns with GDPR and future European digital identity frameworks. This work demonstrates the feasibility and value of a decentralized, patient-centric approach to imaging data governance in high-stakes clinical environments.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0338897.t001（source: https://plos.figshare.com/articles/dataset/_p_Comparison_of_our_work_with_existing_literature_p_/31806064）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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