Evidence map›Paper›PMID 41143164›Full record

ArticleJournal of geriatric cardiology : JGC2025

Performance assessment of computed tomographic angiography fractional flow reserve using deep learning: SMART trial summary.

Wei Zhang, You-Bing Yin, Zhi-Qiang Wang, Ying-Xin Zhao, Dong-Mei Shi, Yong-He Guo, Zhi-Ming Zhou, Zhi-Jian Wang, Shi-Wei Yang, De-An Jia and 2 more

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Article in Journal of geriatric cardiology : JGC, 2025. 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
–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

1 citing paper in PubMed.

  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.

Wei ZhangDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.
You-Bing YinKeya Medical Co., Ltd, Shenzhen, China.
Zhi-Qiang WangDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.
Ying-Xin ZhaoDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.
Dong-Mei ShiDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.
Yong-He GuoDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.
Zhi-Ming ZhouDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.
Zhi-Jian WangDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.
Shi-Wei YangDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.
De-An JiaDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.
Li-Xia YangDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.
Yu-Jie ZhouDepartment of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical center for coronary heart disease, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Non-invasive computed tomography angiography (CTA)-based fractional flow reserve (CT-FFR) could become a gatekeeper to invasive coronary angiography. Deep learning (DL)-based CT-FFR has shown promise when compared to invasive FFR. To evaluate the performance of a DL-based CT-FFR technique, DeepVessel FFR (DVFFR). Methods: This retrospective study was designed for iScheMia Assessment based on a Retrospective, single-center Trial of CT-FFR (SMART). Patients suspected of stable coronary artery disease (CAD) and undergoing both CTA and invasive FFR examinations were consecutively selected from the Beijing Anzhen Hospital between January 1, 2016 to December 30, 2018. FFR obtained during invasive coronary angiography was used as the reference standard. DVFFR was calculated blindly using a DL-based CT-FFR approach that utilized the complete tree structure of the coronary arteries. Results: Three hundred and thirty nine patients (60.5 ±10.0 years and 209 men) and 414 vessels with direct invasive FFR were included in the analysis. At per-vessel level, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of DVFFR were 94.7%, 88.6%, 90.8%, 82.7%, and 96.7%, respectively. The area under the receiver operating characteristics curve (AUC) was 0.95 for DVFFR and 0.56 for CTA-based assessment with a significant difference ( Conclusions: The results demonstrate that DVFFR was able to evaluate lesion hemodynamic significance accurately and effectively with improved diagnostic performance over CTA alone. Coronary artery disease (CAD) is a critical disease in which coronary artery luminal narrowing may result in myocardial ischemia. Early and effective assessment of myocardial ischemia is essential for optimal treatment planning so as to improve the quality of life and reduce medical costs.

Identifiers

PMID41143164
PMCPMC12547420

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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.