Evidence map›Paper›PMID 39449122›Full record

ArticleBMC medical imaging2024

CT coronary fractional flow reserve based on artificial intelligence using different software: a repeatability study.

Jing Li, Zhenxing Yang, Zhenting Sun, Lei Zhao, Aishi Liu, Xing Wang, Qiyu Jin, Guoyu Zhang

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Jing Li *Department of Radiology, the Affiliated Hospital of Inner Mongolia Medical University, No.1 Tongdao North Street, Hohhot, Inner Mongolia, 010050, China.
Zhenxing Yang *Department of Radiology, the Affiliated Hospital of Inner Mongolia Medical University, No.1 Tongdao North Street, Hohhot, Inner Mongolia, 010050, China.
Zhenting SunDepartment of Radiology, the Affiliated Hospital of Inner Mongolia Medical University, No.1 Tongdao North Street, Hohhot, Inner Mongolia, 010050, China.
Lei ZhaoDepartment of Radiology, the Affiliated Hospital of Inner Mongolia Medical University, No.1 Tongdao North Street, Hohhot, Inner Mongolia, 010050, China. 281005117@qq.com.
Aishi LiuDepartment of Radiology, the Affiliated Hospital of Inner Mongolia Medical University, No.1 Tongdao North Street, Hohhot, Inner Mongolia, 010050, China. liuas2024@163.com.
Xing WangDepartment of Basic Medicine College, Inner Mongolia Medical University, No.5 Tongdao North Street, Hohhot, Inner Mongolia, 010059, China.
Qiyu JinDepartment of Mathematics College, Inner Mongolia University, Daxue West Street, Hohhot, Inner Mongolia, 010020, China.
Guoyu ZhangDepartment of Mathematics College, Inner Mongolia University, Daxue West Street, Hohhot, Inner Mongolia, 010020, China.

Funding

The Inner Mongolia Medical University YKD2024QN026
6 · The paper itself

Abstract

objectiveThis study aims to assess the  consistency of various CT-FFR software, to determine the reliability of current CT-FFR software, and to measure relevant influence factors. The goal is to build a solid foundation of enhanced workflow and technical principles that will ultimately improve the accuracy of measurements of coronary blood flow reserve fractions. This improvement is critical for assessing the level of ischemia in patients with coronary heart disease.

methods103 participants were chosen for a prospective research using coronary computed tomography angiography (CCTA) assessment. Heart rate, heart rate variability, subjective picture quality, objective image quality, vascular shifting length, and other factors were assessed. CT-FFR software including K software and S software are used for CT-FFR calculations. The consistency of the two software is assessed using paired-sample t-tests and Bland-Altman plots. The error classification effect is used to construct the receiver operating characteristic curve.

resultsThe CT-FFR measurements differed significantly between the K and S software, with a statistical significance of P < 0.05. In the Bland-Altman plot, 6% of the points (14 out of 216) fell outside the 95% consistency level. Single-factor analysis revealed that heart rate variability, vascular dislocation offset distance, subjective image quality, and lumen diameter significantly influenced the discrepancies in CT-FFR measurements between two software programs (P < 0.05). The ROC curve shows the highest AUC for the vessel shifting length, with an optimal cut-off of 0.85 mm.

conclusionCT-FFR measurements vary among software from different manufacturers, leading to potential misclassification of qualitative diagnostics. Vessel shifting length, subjective image quality score, HRv, and lumen diameter impacted the measurement stability of various software.

Indexed as

Artificial IntelligenceComputed Tomography AngiographyFractional Flow Reserve, MyocardialSoftwareAgedCoronary AngiographyCoronary Artery DiseaseFemaleHumansMaleMiddle AgedProspective StudiesRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsROC CurveCoronary flow reserve fractionDifferenceInfluencing factorsReliability

Identifiers

PMID39449122
PMCPMC11515450

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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.