Evidence map›Paper›PMID 38057491›Full record

ArticleScientific reports2023

Enhancing the image quality of blue light cystoscopy through green-hue correction and fogginess removal.

Shuang Chang, Micha E Bermoy, Sam S Chang, Kristen R Scarpato, Amy N Luckenbaugh, Soheil Kolouri, Audrey K Bowden

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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

7 authors.

Shuang ChangDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37232, USA.
Micha E BermoyDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37232, USA.
Sam S ChangDepartment of Urology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
Kristen R ScarpatoDepartment of Urology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
Amy N LuckenbaughDepartment of Urology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
Soheil KolouriDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, 37232, USA.
Audrey K BowdenDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37232, USA. a.bowden@vanderbilt.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Blue light cystoscopy (BLC) is a guideline-recommended endoscopic tool to detect bladder cancer with high sensitivity. Having clear, high-quality images during cystoscopy is crucial to the sensitive, efficient detection of bladder tumors; yet, important diagnostic information is often missed or poorly visualized in images containing illumination artifacts or impacted by impurities in the bladder. In this study, we introduce computational methods to remove two common artifacts in images from BLC videos: green hue and fogginess. We also evaluate the effect of artifact removal on the perceptual quality of the BLC images through a survey study and computation of Blind/Referenceless Image Spatial Quality Evaluator scores on the original and enhanced images. We show that corrections and enhancements made to cystoscopy images resulted in a better viewing experience for clinicians during BLC imaging and reliably restored lost tissue features that were important for diagnostics. Incorporating these enhancements during clinical and OR procedures may lead to more comprehensive tumor detection, fewer missed tumors during TURBT procedures, more complete tumor resection and shorter procedure time. When used in off-line review of cystoscopy videos, it may also better guide surgical planning and allow more accurate assessment and diagnosis.

Indexed as

Aminolevulinic AcidUrinary Bladder NeoplasmsCystectomyCystoscopyHumansAminolevulinic Acid

Identifiers

PMID38057491
PMCPMC10700554

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.