Evidence map›Paper›PMID 39023665›Full record

ArticleLa Radiologia medica2024

Deep learning reconstruction for coronary CT angiography in patients with origin anomaly, stent or bypass graft.

Lihua Yu, Yarong Yu, Meiling Li, Runjianya Ling, Yuehua Li, Ai Wang, Xifu Wang, Yanli Song, Xiao Zhang, Pei Dong and 3 more

Abstract read
PubMed Publisher
In one paragraph

Article in La Radiologia medica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Article
  7. Article
  8. Review
  9. Review
  10. 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

13 authors.

Lihua Yu *Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, 200080, China.
Yarong Yu *Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, 200080, China.
Meiling LiDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, 200080, China.
Runjianya LingInstitute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, #600, Yishan Rd, Shanghai, China.
Yuehua LiInstitute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, #600, Yishan Rd, Shanghai, China.
Ai WangDepartment of Radiology, Shanghai General Hospital, Jiading Branch, #800, Huangjiahuayuan Rd, Shanghai, China.
Xifu WangDepartment of Radiology, Shanghai General Hospital, Jiading Branch, #800, Huangjiahuayuan Rd, Shanghai, China.
Yanli SongShanghai United Imaging Intelligence Co., Ltd, #2879 Longteng Ave, Shanghai, China.
Xiao ZhangSchool of Information Science and Technology, Northwest University, Xi'an, China.
Pei DongShanghai United Imaging Intelligence Co., Ltd, #2879 Longteng Ave, Shanghai, China.
Yiqiang ZhanShanghai United Imaging Intelligence Co., Ltd, #2879 Longteng Ave, Shanghai, China.
Dijia WuShanghai United Imaging Intelligence Co., Ltd, #2879 Longteng Ave, Shanghai, China.
Jiayin ZhangDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, #85 Wujin Rd, Shanghai, 200080, China. andrewssmu@msn.com.ORCID http://orcid.org/0000-0001-7383-7571

Funding

National Key Research and Development Program of China 2021YFF0501402Shanghai Health Commission Discipline Leader Project 2022XD031Shanghai Jiao Tong University "Star Project" of Biomedical Multi-discipline Research Program YG2022ZD015Shenkang 3-year project of clinical innovation SHDC2022CRD016
6 · The paper itself

Abstract

purposeTo develop and validate a deep learning (DL)-model for automatic reconstruction for coronary CT angiography (CCTA) in patients with origin anomaly, stent or bypass graft. MATERIAL AND

methodsIn this retrospective study, a DL model for automatic CCTA reconstruction was developed with training and validation sets from 6063 and 1962 patients. The algorithm was evaluated on an independent external test set of 812 patients (357 with origin anomaly or revascularization, 455 without). The image quality of DL reconstruction and manual reconstruction (using dedicated cardiac reconstruction software provided by CT vendors) was compared using a 5-point scale. The successful reconstruction rates and post-processing time for two methods were recorded.

resultsIn the external test set, 812 patients (mean age, 64.0 ± 11.6, 100 with origin anomalies, 152 with stents, 105 with bypass grafts) were evaluated. The successful rates for automatic reconstruction were 100% (455/455), 97% (97/100), 100% (152/152), and 76.2% (80/105) in patients with native vessel, origin anomaly, stent, and bypass graft, respectively. The image quality scores were significantly higher for DL reconstruction than those for manual approach in all subgroups (4 vs. 3 for native vessel, 4 vs. 4 for origin anomaly, 4 vs. 3 for stent and 4 vs. 3 for bypass graft, all p < 0.001). The overall post-processing time was remarkably reduced for DL reconstruction compared to manual method (11 s vs. 465 s, p < 0.001).

conclusionsThe developed DL model enabled accurate automatic CCTA reconstruction of bypass graft, stent and origin anomaly. It significantly reduced post-processing time and improved clinical workflow.

Indexed as

Computed Tomography AngiographyCoronary AngiographyDeep LearningStentsAgedCoronary Artery BypassCoronary Vessel AnomaliesFemaleHumansMaleMiddle AgedRetrospective StudiesArtificial intelligenceBypass graftCoronary anomalyCoronary computed tomography angiographyStent

Identifiers

PMID39023665

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.