Evidence map›Paper›PMID 36911025›Full record

ArticleFrontiers in pediatrics2023

Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation.

Taocui Yan, Jinjie Qin, Yulin Zhang, Qiuni Li, Baoru Han, Xin Jin

Abstract read
In one paragraph

Article in Frontiers in pediatrics, 2023. 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

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Taocui YanMedical Data Science Academy, College of Medical Informatics, Chongqing Medical University, Chongqing, China.
Jinjie QinDepartment of Radiology, Children's Hospital of Chongqing Medical University, Chongqing, China.
Yulin ZhangTechnology Research and Development Department of Chongqing Intech Technology Co., LTD, Chongqing,, China.
Qiuni LiMedical Data Science Academy, College of Medical Informatics, Chongqing Medical University, Chongqing, China.
Baoru HanMedical Data Science Academy, College of Medical Informatics, Chongqing Medical University, Chongqing, China.
Xin JinDepartment of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, National Clinical Research Center for Child Health and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To explore the application of the proposed intelligent image processing method in the diagnosis of aortic coarctation computed tomography angiography (CTA) and to clarify its value in the diagnosis of aortic coarctation based on the diagnosis results. Methods: Fifty-three children with coarctation of the aorta (CoA) and forty children without CoA were selected to constitute the study population. CTA was performed on all subjects. The minimum diameters of the ascending aorta, proximal arch, distal arch, isthmus, and descending aorta were measured using manual and intelligent methods, respectively. The Wilcoxon signed-rank test was used to analyze the differences between the two measurements. The surgical diagnosis results were used as the gold standard, and the diagnostic results obtained by the two measurement methods were compared with the gold standard to quantitatively evaluate the diagnostic results of CoA by the two measurement methods. The Kappa test was used to analyze the consistency of intelligence diagnosis results with the gold standard. Results: Whether people have CoA or not, there was a significant difference ( Conclusion: The proposed intelligent method based on image processing can be successfully applied to assist in the diagnosis of CoA.

Indexed as

auxiliary diagnosiscoarctation of the aorta (COA)computed tomography angiography (CTA)intelligent image processingintelligent measurement

Identifiers

PMID36911025
PMCPMC9996173

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