Evidence map›Paper›PMID 39043766›Full record

ArticleScientific reports2024

AI-based segmentation of renal enhanced CT images for quantitative evaluate of chronic kidney disease.

Hui Luo, Jingzhen Li, Haiyang Huang, Lianghong Jiao, Siyuan Zheng, Yibo Ying, Qiang Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
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

7 authors.

Hui Luo *Department of Radiology, Ningbo Yinzhou Second Hospital, Ningbo, China.
Jingzhen Li *Department of Nephrology, Ningbo Yinzhou Second Hospital, Ningbo, China.
Haiyang HuangDepartment of Radiology, Ningbo Yinzhou Second Hospital, Ningbo, China.
Lianghong JiaoDepartment of Radiology, Ningbo Yinzhou Second Hospital, Ningbo, China.
Siyuan ZhengDepartment of Radiology, Ningbo Yinzhou Second Hospital, Ningbo, China.
Yibo YingDepartment of Radiology, Ningbo Yinzhou Second Hospital, Ningbo, China.
Qiang LiDepartment of Radiology, The Affiliated People's Hospital of Ningbo University, Ningbo, 315000, China. rmliqiang@nbu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To quantitatively evaluate chronic kidney disease (CKD), a deep convolutional neural network-based segmentation model was applied to renal enhanced computed tomography (CT) images. A retrospective analysis was conducted on a cohort of 100 individuals diagnosed with CKD and 90 individuals with healthy kidneys, who underwent contrast-enhanced CT scans of the kidneys or abdomen. Demographic and clinical data were collected from all participants. The study consisted of two distinct stages: firstly, the development and validation of a three-dimensional (3D) nnU-Net model for segmenting the arterial phase of renal enhanced CT scans; secondly, the utilization of the 3D nnU-Net model for quantitative evaluation of CKD. The 3D nnU-Net model achieved a mean Dice Similarity Coefficient (DSC) of 93.53% for renal parenchyma and 81.48% for renal cortex. Statistically significant differences were observed among different stages of renal function for renal parenchyma volume (V

Indexed as

KidneyRenal Insufficiency, ChronicTomography, X-Ray ComputedAdultAgedContrast MediaFemaleHumansImaging, Three-DimensionalMaleMiddle AgedNeural Networks, ComputerRetrospective StudiesContrast MediaArtificial intelligenceChronic kidney diseaseComputed tomographyDeep convolutional neural networkRenal function

Identifiers

PMID39043766
PMCPMC11266695

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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 OpenQuestion graph.