Evidence map›Paper›PMID 25037715›Full record

ArticleJournal of medical systems2014

Video summarization based tele-endoscopy: a service to efficiently manage visual data generated during wireless capsule endoscopy procedure.

Irfan Mehmood, Muhammad Sajjad, Sung Wook Baik

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In one paragraph

Article in Journal of medical systems, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Automated overview of complete endoscopies with unsupervised learned descriptors.International journal of computer assisted radiology and surgery · 2026
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4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

3 authors.

Irfan MehmoodCollege of Electronics and Information Engineering, Sejong University, Seoul, Republic of Korea, irfanmehmood@sju.ac.kr.
Muhammad Sajjad
Sung Wook Baik

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use. More importantly, WCE combined with mobile computing ensures rapid transmission of diagnostic data to hospitals and enables off-site senior gastroenterologists to offer timely decision making support. However, during this WCE process, video data are produced in huge amounts, but only a limited amount of data is actually useful for diagnosis. The sharing and analysis of this video data becomes a challenging task due the constraints such as limited memory, energy, and communication capability. In order to facilitate efficient WCE data collection and browsing tasks, we present a video summarization-based tele-endoscopy service that estimates the semantically relevant video frames from the perspective of gastroenterologists. For this purpose, image moments, curvature, and multi-scale contrast are computed and are fused to obtain the saliency map of each frame. This saliency map is used to select keyframes. The proposed tele-endoscopy service selects keyframes based on their relevance to the disease diagnosis. This ensures the sending of diagnostically relevant frames to the gastroenterologist instead of sending all the data, thus saving transmission costs and bandwidth. The proposed framework also saves storage costs as well as the precious time of doctors in browsing patient's information. The qualitative and quantitative results are encouraging and show that the proposed service provides video keyframes to the gastroenterologists without discarding important information.

Indexed as

Capsule EndoscopyRemote ConsultationAlgorithmsComputer SystemsData DisplayHumansImage Interpretation, Computer-Assisted

Identifiers

What OpenQuestion holds

Textmetadata
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.