ArticleJournal of healthcare engineering2022
Analysis of Cardiovascular Disease Angiography Process Based on Rough Set and Internet of Things.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Retracted: Analysis of Cardiovascular Disease Angiography Process Based on Rough Set and Internet of Things.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-12-06Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The angiography image enhancement technology has the potential to enhance the vascular structure in the image while suppressing the background and nonvascular structures simultaneously. This technology has the ability to enhance the result as close to the real structure of blood vessels as possible. Angiographic image processing is one of the essential contents in the field of medical image processing and analysis. However, the existing cardiovascular angiography schemes suffer from various issues. In this paper, the detection process of cardiovascular angiography is studied by combining the Internet of Things and rough set technology. Firstly, this paper designs the architecture design of the cardiovascular angiography process combined with the Internet of Things technology. Secondly, this paper uses a rough set algorithm to optimize the background noise and boundary shrinkage because of the sensitivity of the contrast background noise and boundary shrinkage. Simulation results verified the applicability and efficiency of the proposed model in the cardiovascular angiography scheme. The model has been optimized during implementation. Compared with the traditional algorithm, the same image data processing speed is significantly improved to ensure the enhancement effect.
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Registered trials
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.