Evidence map›Paper›PMID 42626266›Full record

ArticleJournal of the Society for Cardiovascular Angiography & Interventions2026

Artificial Intelligence-Derived Fractional Flow Reserve in Routine Clinical Practice: An International Multicenter Retrospective Study.

Eyal Ben-Assa, Bruce A Samuels, Ehtisham Mahmud, Carlos Cafri, Ariel Roguin, Yair Feld, Eli Lev, Emanuel Harari, Tali Ben-Yehuda, Hector M Garcia-Garcia and 1 more

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In one paragraph

Article in Journal of the Society for Cardiovascular Angiography & Interventions, 2026. 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

11 authors.

Eyal Ben-AssaAssuta Ashdod University Hospital, Ben-Gurion University of the Negev, Ashdod, Israel.
Bruce A SamuelsCedars-Sinai Medical Center, Los Angeles, California.
Ehtisham MahmudSulpizio Cardiovascular Institute, University of California, San Diego, La Jolla, California.
Carlos CafriSoroka Medical Center, Ben-Gurion University of the Negev, Beer Sheva, Israel.
Ariel RoguinHillel Yaffe Medical Center, Hadera, Ruth and Bruce Rappaport Faculty of Medicine, Technion Institute of Technology, Haifa, Israel.
Yair FeldTzafon (Poriya) Medical Center, Azrieli Faculty of Medicine, Bar Ilan University, Tveria, Israel.
Eli LevAssuta Ashdod University Hospital, Ben-Gurion University of the Negev, Ashdod, Israel.
Emanuel HarariAssuta Ashdod University Hospital, Ben-Gurion University of the Negev, Ashdod, Israel.
Tali Ben-YehudaSulpizio Cardiovascular Institute, University of California, San Diego, La Jolla, California.
Hector M Garcia-GarciaMedStar Washington Hospital Center, Washington, DC.
Gregg W StoneIcahn School of Medicine at Mount Sinai, New York, New York.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence-based fractional flow reserve (AI-FFR) incorporates a machine learning-based algorithm to derive FFR directly from angiography. Its accuracy compared with invasive FFR has not been assessed. Methods: AI-FFR was compared with wire-based FFR in patients with a single intermediate lesion (diameter stenosis ≥40% to <70%) at 5 centers in the United States and Israel. AI-FFR assessments were performed by core laboratory analysts blinded to invasive FFR results. Diagnostic performance metrics were calculated using an FFR threshold of ≤0.80. Results: A total of 504 vessels from 496 patients were analyzed. AI-FFR computation time was 36.1 ± 7.7 seconds. Lesion detection was fully automatic in 371 of 504 (73.6%) vessels, whereas semiautomated analysis with manual marking was performed in 133 of 504 (26.4%) vessels. Mean wire-based FFR was 0.85 ± 0.07 and mean AI-FFR was 0.85 ± 0.08 (mean difference, 0.00 ± 0.08; 95% CI, -0.15 to 0.15; Conclusions: AI-FFR, an automated, machine learning-based tool, demonstrated high diagnostic accuracy compared with wire-based FFR. Its speed, simplicity, and independence from complex procedural steps may facilitate broader adoption during coronary angiography.

Indexed as

angiographyartificial intelligenceartificial intelligence-derived fractional flow reservefractional flow reserve

Identifiers

PMID42626266
PMCPMC13491340

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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.