Evidence map›Paper›PMID 36713258›Full record

ArticleArXiv2023

Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence.

Okyaz Eminaga, Timothy Jiyong Lee, Jessie Ge, Eugene Shkolyar, Mark Laurie, Jin Long, Lukas Graham Hockman, Joseph C Liao

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Okyaz EminagaDepartment of Urology, Stanford University School of Medicine, Stanford.
Timothy Jiyong LeeDepartment of Urology, Stanford University School of Medicine, Stanford.
Jessie GeDepartment of Urology, Stanford University School of Medicine, Stanford.
Eugene ShkolyarDepartment of Urology, Stanford University School of Medicine, Stanford.
Mark LaurieDepartment of Urology, Stanford University School of Medicine, Stanford.
Jin LongCenter for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA.
Lukas Graham HockmanDepartment of Urology, Stanford University School of Medicine, Stanford.
Joseph C LiaoDepartment of Urology, Stanford University School of Medicine, Stanford.

Funding

Intraoperative integration of artificial intelligence during cystoscopic surgeryR01CA260426 · NCI · STANFORD UNIVERSITY · PI JOSEPH C LIAO, Lei Xing · 2022 to 2026
$2.3M
NCI NIH HHS R01 CA260426
6 · The paper itself

Abstract

backgroundThe 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.

methodsA 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.

resultsThe 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.

conclusionOur 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.

Identifiers

PMID36713258
PMCPMC9882574

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.