Evidence map›Paper›PMID 39943388›Full record

ArticleSensors (Basel, Switzerland)2025

Localization of Capsule Endoscope in Alimentary Tract by Computer-Aided Analysis of Endoscopic Images.

Ruiyao Zhang, Boyuan Peng, Yiyang Liu, Xinkai Liu, Jie Huang, Kohei Suzuki, Yuki Nakajima, Daiki Nemoto, Kazutomo Togashi, Xin Zhu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

10 authors.

Ruiyao ZhangGraduate Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan.
Boyuan PengGraduate Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan.ORCID 0009-0002-9226-6406
Yiyang LiuGraduate Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan.
Xinkai LiuGraduate Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan.
Jie HuangGraduate Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan.
Kohei SuzukiDivision of Coloproctology, Aizu Medical Center, Fukushima Medical University, Aizuwakamatsu 969-3492, Japan.ORCID 0009-0009-0105-3036
Yuki NakajimaDivision of Coloproctology, Aizu Medical Center, Fukushima Medical University, Aizuwakamatsu 969-3492, Japan.
Daiki NemotoDivision of Coloproctology, Aizu Medical Center, Fukushima Medical University, Aizuwakamatsu 969-3492, Japan.ORCID 0000-0002-6812-2966
Kazutomo TogashiDivision of Coloproctology, Aizu Medical Center, Fukushima Medical University, Aizuwakamatsu 969-3492, Japan.
Xin ZhuDepartment of AI Technology Development, M&D Data Science Center, Institute of Integrated Research, Institute of Science Tokyo, Chiyoda, Tokyo 101-0062, Japan.ORCID 0000-0002-4376-0806

Funding

Competitive Research Fund from The University of Aizu 2024-P-7
6 · The paper itself

Abstract

Capsule endoscopy is a common method for detecting digestive diseases. The location of a capsule endoscope should be constantly monitored through a visual inspection of the endoscopic images by medical staff to confirm the examination's progress. In this study, we proposed a computer-aided analysis (CADx) method for the localization of a capsule endoscope. At first, a classifier based on a Swin Transformer was proposed to classify each frame of the capsule endoscopy videos into images of the stomach, small intestine, and large intestine, respectively. Then, a K-means algorithm was used to correct outliers in the classification results. Finally, a localization algorithm was proposed to determine the position of the capsule endoscope in the alimentary tract. The proposed method was developed and validated using videos of 204 consecutive cases. The proposed CADx, based on a Swin Transformer, showed a precision of 93.46%, 97.28%, and 98.68% for the classification of endoscopic images recorded in the stomach, small intestine, and large intestine, respectively. Compared with the landmarks identified by endoscopists, the proposed method demonstrated an average transition time error of 16.2 s to locate the intersection of the stomach and small intestine, as well as 13.5 s to locate that of the small intestine and the large intestine, based on the 20 validation videos with an average length of 3261.8 s. The proposed method accurately localizes the capsule endoscope in the alimentary tract and may replace the laborious real-time visual inspection in capsule endoscopic examinations.

Indexed as

Capsule EndoscopesCapsule EndoscopyGastrointestinal TractImage Processing, Computer-AssistedAlgorithmsHumansIntestine, SmallStomachcapsule endoscopycomputer-aided analysisdeep learningtransformer

Identifiers

PMID39943388
PMCPMC11820705

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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.