Evidence map›Paper›PMID 42222137›Full record

ArticleFrontiers in radiology2026

Application of AIIR algorithm for quality improvement and noise reduction in pediatric abdominal contrast-enhanced CT.

Xie Jiazhi, Dai Dajian, Tang Shilong, Fan Xiao, Zhu Luyao, He Ling

Abstract read
In one paragraph

Article in Frontiers in radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

6 authors.

Xie JiazhiDepartment of Radiology, Children's Hospital Affiliated to Chongqing Medical University, Chongqing, China.
Dai DajianDepartment of Radiology, Children's Hospital Affiliated to Chongqing Medical University, Chongqing, China.
Tang ShilongDepartment of Radiology, Children's Hospital Affiliated to Chongqing Medical University, Chongqing, China.
Fan XiaoDepartment of Radiology, Children's Hospital Affiliated to Chongqing Medical University, Chongqing, China.
Zhu LuyaoCT Business Unit, Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China.
He LingDepartment of Radiology, Children's Hospital Affiliated to Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the feasibility and value of the deep learning full model iterative algorithm (AIIR) in reducing radiation dose during contrast-enhanced whole abdominal CT scans in children. Methods: Data from 100 pediatric patients undergoing contrast-enhanced whole abdominal CT scans due to clinical indications were retrospectively collected. The patients were divided into a conventional group ( Results: The experimental group exhibited significantly lower radiation dose compared to the conventional group (ED reduction by 23.61%, Conclusion: The AIIR algorithm achieves superior image quality at low dose levels (80 kVp) compared to conventional HIR and low-dose HIR protocols, demonstrating significant clinical value and potential for widespread application in pediatric abdominal imaging.

Indexed as

abdominal CTchildrencontrast agentdeep learning iterative reconstructionradiation dose

Identifiers

PMID42222137
PMCPMC13219036

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.