Evidence map›Paper›PMID 31080936›Full record

ArticleLaparoscopic, endoscopic, and robotic surgery2018

Real-time computerized video enhancement for minimally invasive fetoscopic surgery.

Praneeth Sadda, John Onofrey, Metehan Imamoglu, Xenophon Papademetris, Bilal Qarni, Mert Ozan Bahtiyar

Open access · diamondAbstract read
In one paragraph

Article in Laparoscopic, endoscopic, and robotic surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 45% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

6 authors at 3 institutions in 2 countries.

Praneeth SaddaDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
John OnofreyDepartment of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
Metehan ImamogluDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
Xenophon PapademetrisDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
Bilal QarniUniversity of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada.
Mert Ozan BahtiyarDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
Yale University · USYale New Haven Hospital · USWestern University · CA

Funding

Short Term Research Training: Students in Health Professional SchoolsT35DK104689 · NIDDK · YALE UNIVERSITY · PI LLOYD G CANTLEY, Sarwat I Chaudhry · 2015 to 2026
$2.6M
NIDDK NIH HHS T35 DK104689
6 · The paper itself

Abstract

backgroundThe only definitive treatment for twin-to-twin transfusion syndrome is minimally invasive fetoscopic surgery for the selective coagulation of placental blood vessels. Fetoscopic surgery is a technically challenging operation, mainly due to the poor visibility conditions in the uterine environment. We present the design of an algorithm for the computerized enhancement of fetoscopic video and show that the enhanced video increases the ability of human users to identify blood vessels within fetoscopic video rapidly and accurately.

methodsA computer algorithm for the enhancement of fetoscopic video frames was created. First, optical fiber artifacts were removed via a modification of unsharp masking. Second, image contrast was increased via Contrast Limited Adaptive Histogram Equalization (CLAHE). Third, the effect of contrast enhancements on stationary features was removed by normalizing to a windowed mean of the video frames. Fourth, color information was reincorporated by combining the mean-normalized result with the unnormalized contrast enhanced image using the soft light blending algorithm. Medical trainees (

resultsOn the subset of images where subjects had the lowest mean accuracy in identifying the placement of the marker, subjects performed better when viewing video frames enhanced by the computer (accuracy 74.27%; SE 0.97) than when viewing unenhanced video frames (accuracy 63.78%; SE 2.79). This result was statistically significant (

conclusionReal-time computerized enhancement of fetoscopic video has the potential to ease the readability of video in poor lighting conditions, thus providing a benefit to the surgeon intraoperatively.

Identifiers

PMID31080936
PMCPMC6508886
OpenAlexW2809324798

What OpenQuestion holds

Textmetadata
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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.