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Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence
Okyaz Eminaga1,2, Timothy Jiyong Lee1, Jessie Ge1
1Department of Urology, Stanford University School of Medicine, Stanford.
Arxiv
|January 30, 2023
Summary
This study introduces a framework for standardized cystoscopy documentation, enabling efficient data management for education and AI research. The approach ensures data is findable, accessible, interoperable, and reusable (FAIR).
Area of Science:
- Urology
- Medical Informatics
- Data Science
Background:
- Clinical documentation of cystoscopy generates visual and textual data.
- Secondary use of visual cystoscopic data for education and research is limited by inefficient clinical data management.
Approach:
- A conceptual framework was designed for standardized cystoscopy documentation, encompassing data, annotation, and utilization management.
- A Swiss-cheese model was proposed for quality control and root cause analysis.
- The framework's infrastructure was defined adhering to FAIR (findable, accessible, interoperable, reusable) principles.
Key Points:
- The framework successfully implemented FAIR principles for cystoscopy data.
- A cystoscopy atlas was created, suitable for an educational web portal.
- 68 qualitative videos and annotation data were made available for AI projects (frame classification, segmentation).
Conclusions:
- The proposed framework standardizes visual documentation storage.
- It enables FAIR data sharing for educational purposes and artificial intelligence research in urology.
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