Evidence map›Paper›PMID 39046956›Full record

ArticlePloS one2024

A modified Tseng algorithm approach to restoring thoracic diseases' computerized tomography images.

Dilber Uzun Ozsahin, Abubakar Adamu, Maryam Rabiu Aliyu, Huzaifa Umar

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Dilber Uzun OzsahinDepartment of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah, UAE.
Abubakar AdamuOperational Research Center in Healthcare, Near East University, Nicosia, Turkey.
Maryam Rabiu AliyuDepartment of Energy System Engineering, Cyprus International University, Nicosia, Turkey.
Huzaifa UmarOperational Research Center in Healthcare, Near East University, Nicosia, Turkey.ORCID 0000-0003-2508-9710

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is well-known that the Tseng algorithm and its modifications have been successfully employed in approximating zeros of the sum of monotone operators. In this study, we restored various thoracic diseases' computerized tomography (CT) images, which were degraded with a known blur function and additive noise, using a modified Tseng algorithm. The test images used in the study depict calcification of the Aorta, Subcutaneous Emphysema, Tortuous Aorta, Pneumomediastinum, and Pneumoperitoneum. Additionally, we employed well-known image restoration tools to enhance image quality and compared the quality of restored images with the originals. Finally, the study demonstrates the potential to advance monotone inclusion problem-solving, particularly in the field of medical image recovery.

Indexed as

AlgorithmsThoracic DiseasesTomography, X-Ray ComputedHumans

Identifiers

PMID39046956
PMCPMC11268622

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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.