ArticleOpen heart2022
Interoperator reliability of an on-site machine learning-based prototype to estimate CT angiography-derived fractional flow reserve.
Article in Open heart, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence based fractional flow reserve.Cardiology journal · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdvances in CT and machine learning have enabled on-site non-invasive assessment of fractional flow reserve (FFR PURPOSE: To assess the interoperator and intraoperator variability of coronary CT angiography-derived FFR MATERIALS AND
methodsWe included 60 symptomatic patients who underwent coronary CT angiography. FFR
resultsA total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI 0.977 to 0.992) and 0.972 per segment (95% CI 0.967 to 0.977). The absolute mean difference in FFR
conclusionA high degree of interoperator and intraoperator reproducibility can be achieved by on-site machine learning-based FFR
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Registered trials
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