Evidence map›Paper›PMID 38041712›Full record

ReviewPediatric radiology2024

Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.

Ismail Mese, Ceren Altintas Mese, Ugur Demirsoy, Yonca Anik

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pediatric radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
4.8field-weighted citation impact, top 4% of its field
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 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
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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

4 authors at 3 institutions in 2 countries.

Ismail MeseDepartment of Radiology, Health Sciences University, Erenkoy Mental Health and Neurology Training and Research Hospital, 19 Mayis, Sinan Ercan Cd. No:23, Kadikoy, Istanbul, 34736, Turkey. ismail_mese@yahoo.com.ORCID 0000-0002-4429-6996
Ceren Altintas MeseDepartment of Pediatrics, Haydarpasa Numune Training and Research Hospital, Istanbul, Turkey.ORCID 0000-0003-4918-590X
Ugur DemirsoyDepartment of Pediatric Oncology, Faculty of Medicine, Kocaeli University, Kocaeli, Turkey.ORCID 0000-0002-5424-7215
Yonca AnikDepartment of Pediatric Radiology, Faculty of Medicine, Kocaeli University, Kocaeli, Turkey.ORCID 0000-0002-6768-2574
Kocaeli Üniversitesi · TRHaydarpaşa Numune Eğitim ve Araştırma Hastanesi · TRUniversity of Health Science · KH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology's safety and effectiveness.

Indexed as

RadiologyTomography, X-Ray ComputedAlgorithmsChildHumansImage Processing, Computer-AssistedRadiation DosageRadiographic Image Interpretation, Computer-AssistedSoftwareChildComputed tomographyComputer assisted image processingDeep learningRadiation monitoring

Identifiers

PMID38041712
OpenAlexW4389273849

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.