ReviewCardiovascular ultrasound2026
Quantification of mitral regurgitation: from traditional methods to artificial intelligence.
Review in Cardiovascular ultrasound, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mitral regurgitation (MR) is a common valvular disorder and associated with adverse outcomes. Echocardiography is the primary imaging modality for MR assessment; however, standard methods rely on geometric assumptions and single-frame analysis, which are especially inaccurate in the setting of eccentric, multiple or non-holosystolic jets. Recent developments in machine learning have enabled automated quantification of MR, which analyse regurgitant flow throughout systole, account for non-hemispheric orifice area and jet morphology, and demonstrate favourable agreement with cardiac magnetic resonance. In addition, AI capable of detecting and grading MR directly from echocardiographic clips offer potential utility for screening, particularly in low-resource settings where specialist review is limited. Quantification of Mitral Regurgitation: from traditional methods to Artificial Intelligence.
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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.