Evidence map›Paper›PMID 37393527›Full record

ArticleThe British journal of radiology2023

Feasibility of a deep learning algorithm to achieve the low-dose

Lin Liu, Xingyu Chen, Liwen Wan, Na Zhang, Ruibao Hu, Wenbo Li, Shengping Liu, Yan Zhu, Hua Pang, Dong Liang and 2 more

Open access · bronzeAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 32% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

12 authors at 5 institutions in 1 country.

Lin LiuDepartment of Nuclear Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Xingyu ChenLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Liwen WanLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Na ZhangLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Ruibao HuLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Wenbo LiDepartment of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Shengping LiuChongqing University of Technology, Chongqing, China.
Yan ZhuDepartment of Nuclear Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Hua PangDepartment of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Dong LiangLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Yue ChenDepartment of Nuclear Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Zhanli HuLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.ORCID 0000-0003-0618-6240
Shenzhen Institutes of Advanced Technology · CNChongqing University of Technology · CNFirst Affiliated Hospital of Sichuan Medical University · CNThe Affiliated Yongchuan Hospital of Chongqing Medical University · CNAffiliated Hospital of Southwest Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesOur work aims to study the feasibility of a deep learning algorithm to reduce the

methodsThe data of 130 patients who underwent

resultsData showed that the DL-33% images of the two test data sets met the clinical diagnosis requirements, and the overall lesion detection rate of the two centers reached 95.9%.

conclusionThrough deep learning, we demonstrated that reducing the ADVANCES IN KNOWLEDGE: This is the first study of low-dose

Indexed as

Deep LearningGallium RadioisotopesAlgorithmsFeasibility StudiesFluorodeoxyglucose F18HumansPositron-Emission TomographyPositron Emission Tomography Computed TomographyFluorodeoxyglucose F18Gallium Radioisotopes

Identifiers

PMID37393527
PMCPMC10461288
OpenAlexW4382881411

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.