ArticleEuropean radiology2022
Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter study.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Agreement of Fractional Flow Reserve Estimated by Computed Tomography With Invasively Measured Fractional Flow Reserve: A Systematic Review and Meta-Analysis.Journal of the American Heart Association · 2024Pooled it
- Clinical use of coronary computed tomography angiography-derived fractional flow reserve: expert consensus by an International Working Group.European radiology · 2026Article
- Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.Open heart · 2025Review
- Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve: A Comprehensive Review.Reviews in cardiovascular medicine · 2025Review
- Artificial Intelligence based fractional flow reserve.Cardiology journal · 2025Review
- Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review.International journal of cardiology. Heart & vasculature · 2024Article
- Combination of coronary CT angiography, FFRClinical cardiology · 2023Article
- CT-Based Leiden Score Outperforms Confirm Score in Predicting Major Adverse Cardiovascular Events for Diabetic Patients with Suspected Coronary Artery Disease.Korean journal of radiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 9 institutions in 1 country.
Funding
Abstract
objectivesTo examine the diagnostic accuracy of machine learning-based coronary CT angiography-derived fractional flow reserve (FFR
methodsIn total, 484 patients with suspected or known coronary artery disease from 11 Chinese medical centers were retrospectively analyzed. All patients underwent CCTA, FFR
resultsSensitivity, specificity, accuracy, and AUC of FFR
conclusionsDM has no negative impact on the diagnostic accuracy of machine learning-based FFR KEY POINTS: • ML-based FFR
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.