Eminaga Okyaz, Lee Timothy Jiyong, Ge Jessie, Shkolyar Eugene, Laurie Mark, Long Jin, Hockman Lukas Graham, Liao Joseph C
Department of Urology, Stanford University School of Medicine, Stanford.
Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA.
ArXiv. 2023 Jan 18:arXiv:2301.05991v2.
The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice.
A conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, re-usable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure the compliance with FAIR principles.
The framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion and frame levels.
Our study shows that the proposed framework facilitates the storage of the visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.
膀胱镜检查的临床文档包括视觉和文本材料。然而，由于常规临床实践中数据管理效率低下，膀胱镜视觉数据用于教育和研究目的的二次利用仍然有限。
设计了一个概念框架，以标准化方式记录膀胱镜检查，包括三个主要部分：数据管理、注释管理和利用管理。提出了一种瑞士奶酪模型用于质量控制和根本原因分析。我们根据FAIR（可查找、可访问、可互操作、可重用）原则定义了实施该框架所需的基础设施。我们应用了两个场景，举例说明了用于研究和教育项目的数据共享，以确保符合FAIR原则。
该框架在遵循FAIR原则的同时成功实施。从该框架生成的膀胱镜图谱可以在一个教育门户网站上展示；总共68个全长定性视频和相应的注释数据可用于人工智能项目，涵盖病例、病变和帧级别的帧分类和分割问题。
我们的研究表明，所提出的框架有助于以标准化方式存储视觉文档，并为教育和人工智能研究提供符合FAIR原则的数据。
