Evidence map›Paper›PMID 40123453›Full record

ArticleTherapeutic advances in cardiovascular disease

Technical implications of a novel deep learning system in the segmentation and evaluation of computed tomography angiography before transcatheter aortic valve replacement.

Min Jin, Yu Mao, Jian Yang, Jian Liu, Guozhong Chen, Timothée Noterdaeme, Rüdiger Lange, Chenming Ma, Yingqiang Guo, Haibo Zhang

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Therapeutic advances in cardiovascular disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

1 citing paper in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

10 authors.

Min JinDepartment of Cardiothoracic Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.
Yu MaoDepartment of Cardiovascular Surgery, Xijing Hospital, Xi'an, China.
Jian YangDepartment of Cardiovascular Surgery, Xijing Hospital, Xi'an, China.ORCID 0000-0003-2059-1912
Jian LiuDepartment of Cardiac Surgery, Guangdong Provincial People's Hospital, Guangzhou, China.
Guozhong ChenDepartment of Medical Imaging, Nanjing First Hospital, Nanjing, China.
Timothée NoterdaemeDepartment of Cardiology, Hospital of Liege University, Liege, Belgium.
Rüdiger LangeNanjing Saint Medical Technology Co., Ltd., Nanjing, China.
Chenming MaNanjing Saint Medical Technology Co., Ltd., Nanjing, China.
Yingqiang GuoDepartment of Cardiovascular Surgery, West China Hospital, Chengdu, China.
Haibo ZhangDepartment of Cardiovascular Surgery, Beijing Anzhen Hospital, 37 Anzhen Road, Beijing 100029, China.ORCID 0009-0003-1202-5395

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe goal of this study was to compare the computed tomography angiography scans of the segmentation results from the Cvpilot, 3mensio, and Volume Viewer systems to explore the practicability of the Cvpilot system in the automatic segmentation and technical evaluation of the aortic root before transcatheter aortic valve replacement (TAVR).

designA total of 154 patients who underwent TAVR at our center from January 2022 to May 2023 were enrolled, and their computed tomography angiography images were analyzed using the Cvpilot, 3mensio, and Volume Viewer systems, respectively.

settingNot applicable.

participantsNot applicable.

main outcome measuresThe reconstructed computed tomography angiography images were evaluated by experts, and the measurements of the aortic roots were analyzed statistically.

resultsCompared with the 3mensio system, 92.2% of patients (

conclusionOverall, the Cvpilot system is effective and reliable. It can accurately complete the segmentation and the measurement of aortic root structures, thereby effectively improving the measurement quality before TAVR.

trial registrationNot applicable.

Indexed as

Aortic ValveAortic Valve StenosisAortographyComputed Tomography AngiographyDeep LearningRadiographic Image Interpretation, Computer-AssistedTranscatheter Aortic Valve ReplacementAgedAged, 80 and overFemaleHumansMalePredictive Value of TestsReproducibility of ResultsRetrospective StudiesTreatment OutcomeCvpilotdeep learningsegmentationtranscatheter aortic valve replacement

Identifiers

PMID40123453
PMCPMC11938438

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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.