Evidence map›Paper›PMID 36190581›Full record

ArticleJournal of medical systems2022

An Efficient Framework for Video Documentation of Bladder Lesions for Cystoscopy: A Proof-of-Concept Study.

Okyaz Eminaga, T Jessie Ge, Eugene Shkolyar, Mark A Laurie, Timothy J Lee, Lukas Hockman, Xiao Jia, Lei Xing, Joseph C Liao

Abstract read
In one paragraph

Article in Journal of medical systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Okyaz EminagaDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA. okyaz.eminaga@stanford.edu.
T Jessie GeDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Eugene ShkolyarDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Mark A LaurieDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Timothy J LeeDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Lukas HockmanDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Xiao JiaDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Lei XingDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Joseph C LiaoDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA. jliao@stanford.edu.

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 CA260426NIH HHS R01 CA260426
6 · The paper itself

Abstract

Processing full-length cystoscopy videos is challenging for documentation and research purposes. We therefore designed a surgeon-guided framework to extract short video clips with bladder lesions for more efficient content navigation and extraction. Screenshots of bladder lesions were captured during transurethral resection of bladder tumor, then manually labeled according to case identification, date, lesion location, imaging modality, and pathology. The framework used the screenshot to search for and extract a corresponding 10-seconds video clip. Each video clip included a one-second space holder with a QR barcode informing the video content. The success of the framework was measured by the secondary use of these short clips and the reduction of storage volume required for video materials. From 86 cases, the framework successfully generated 249 video clips from 230 screenshots, with 14 erroneous video clips from 8 screenshots excluded. The HIPPA-compliant barcodes provided information of video contents with a 100% data completeness. A web-based educational gallery was curated with various diagnostic categories and annotated frame sequences. Compared with the unedited videos, the informative short video clips reduced the storage volume by 99.5%. In conclusion, our framework expedites the generation of visual contents with surgeon's instruction for cystoscopy and potential incorporation of video data towards applications including clinical documentation, education, and research.

Indexed as

CystoscopyUrinary Bladder NeoplasmsDiagnostic ImagingDocumentationHumansUrinary BladderCystoscopy atlasCystoscopy documentationData distributionData managementVideo-based documentationVideo for cystoscopy

Identifiers

PMID36190581
PMCPMC10751224

What OpenQuestion holds

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Registered trials

None linked

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.