Evidence map›Paper›PMID 30886503›Full record

ArticleWorld journal of gastroenterology2019

Quest for the best endoscopic imaging modality for computer-assisted colonic polyp staging.

Georg Wimmer, Michael Gadermayr, Gernot Wolkersdörfer, Roland Kwitt, Toru Tamaki, Jens Tischendorf, Michael Häfner, Shigeto Yoshida, Shinji Tanaka, Dorit Merhof and 1 more

Abstract read
In one paragraph

Article in World journal of gastroenterology, 2019. 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
–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

1 citing paper in PubMed.

  1. Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease.Frontiers in bioengineering and biotechnology · 2021
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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

11 authors.

Georg WimmerDepartment of Computer Sciences, University of Salzburg, Salzburg 5020, Austria. gwimmer@cosy.sbg.ac.at.
Michael GadermayrInterdisciplinary Imaging and Vision Institute Aachen, RWTH Aachen, Aachen 52074, Germany.
Gernot WolkersdörferDepartment of Internal Medicine I, Paracelsus Medical University/Salzburger Landeskliniken (SALK), Salzburg 5020, Austria.
Roland KwittDepartment of Computer Sciences, University of Salzburg, Salzburg 5020, Austria.
Toru TamakiDepartment of Information Engineering, Graduate School of Engineering, Hiroshima University, Hiroshima 7398527, Japan.
Jens TischendorfInternal Medicine and Gastroenterology, University Hospital Aachen, Würselen 52146, Germany.
Michael HäfnerDepartment of Gastroenterologie and Hepatologie, Krankenhaus St. Elisabeth, Wien 1080, Austria.
Shigeto YoshidaDepartment of Endoscopy and Medicine, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima 7348551, Japan.
Shinji TanakaDepartment of Endoscopy, Hiroshima University Hospital, Hiroshima 7348551, Japan.
Dorit MerhofInterdisciplinary Imaging and Vision Institute Aachen, RWTH Aachen, Aachen 52074, Germany.
Andreas UhlDepartment of Computer Sciences, University of Salzburg, Salzburg 5020, Austria.

Funding

Austrian Science Fund FWF TRP 206
6 · The paper itself

Abstract

backgroundIt was shown in previous studies that high definition endoscopy, high magnification endoscopy and image enhancement technologies, such as chromoendoscopy and digital chromoendoscopy [narrow-band imaging (NBI), i-Scan] facilitate the detection and classification of colonic polyps during endoscopic sessions. However, there are no comprehensive studies so far that analyze which endoscopic imaging modalities facilitate the automated classification of colonic polyps. In this work, we investigate the impact of endoscopic imaging modalities on the results of computer-assisted diagnosis systems for colonic polyp staging.

aimTo assess which endoscopic imaging modalities are best suited for the computer-assisted staging of colonic polyps.

methodsIn our experiments, we apply twelve state-of-the-art feature extraction methods for the classification of colonic polyps to five endoscopic image databases of colonic lesions. For this purpose, we employ a specifically designed experimental setup to avoid biases in the outcomes caused by differing numbers of images per image database. The image databases were obtained using different imaging modalities. Two databases were obtained by high-definition endoscopy in combination with i-Scan technology (one with chromoendoscopy and one without chromoendoscopy). Three databases were obtained by high-magnification endoscopy (two databases using narrow band imaging and one using chromoendoscopy). The lesions are categorized into non-neoplastic and neoplastic according to the histological diagnosis.

resultsGenerally, it is feature-dependent which imaging modalities achieve high results and which do not. For the high-definition image databases, we achieved overall classification rates of up to 79.2% with chromoendoscopy and 88.9% without chromoendoscopy. In the case of the database obtained by high-magnification chromoendoscopy, the classification rates were up to 81.4%. For the combination of high-magnification endoscopy with NBI, results of up to 97.4% for one database and up to 84% for the other were achieved. Non-neoplastic lesions were classified more accurately in general than non-neoplastic lesions. It was shown that the image recording conditions highly affect the performance of automated diagnosis systems and partly contribute to a stronger effect on the staging results than the used imaging modality.

conclusionChromoendoscopy has a negative impact on the results of the methods. NBI is better suited than chromoendoscopy. High-definition and high-magnification endoscopy are equally suited.

Indexed as

Colonic PolypsColonoscopyColorectal NeoplasmsColoring AgentsDiagnosis, Computer-AssistedHumansImage EnhancementNarrow Band ImagingPrecancerous ConditionsVideo RecordingColoring AgentsAutomated diagnosis systemChromoendoscopyColonic polypsEndoscopyImage enhancement technologiesImaging modalitiesNarrow-band imaging

Identifiers

PMID30886503
PMCPMC6421240

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LicenceCC BY-NC
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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.