ArticleJACC. Advances2026
Comparison of Artificial Intelligence-Driven Echocardiographic Assessment of Diastolic Function With the ASE Guidelines.
Article in JACC. Advances, 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
- Update of
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundContemporary guideline-based assessment of left ventricular diastolic function and filling pressure requires multiple echocardiographic parameters that may not be routinely acquired in real-world clinical practice.
objectivesThe aim of the study was to compare the prognostic and diagnostic performance of a previously validated deep learning (DL) model for left ventricular diastolic function assessment with those of the 2016 ASE (American Society of Echocardiography)/EACVI (European Association of Cardiovascular Imaging) and 2025 ASE guidelines.
methodsThe DL model and the guidelines were compared in the ARIC (Atherosclerosis Risk In Communities) cohort (n = 5,450) for prognostication and in 2 hemodynamic validation cohorts from the United States (n = 83) and Japan (n = 130) for diagnosing elevated left ventricular filling pressure. In all 3 cohorts, ≥1 guideline-recommended parameters were unavailable in a substantial proportion of patients.
resultsIn the ARIC cohort, the DL model achieved a higher C-index than both the 2016 ASE/EACVI and 2025 ASE guidelines (0.676 vs 0.638 and 0.602, respectively; both P < 0.001). In the United States cohort, the DL model achieved a higher area under the receiver operating characteristic curve (AUC) than the 2025 ASE guidelines (0.879 vs 0.822; P = 0.041) and a similar AUC to the 2016 ASE/EACVI guidelines (0.879 vs 0.812; P = 0.138). In the Japanese cohort, the DL model achieved a higher AUC than both guideline versions (0.816 vs 0.694 and 0.634, respectively; both P < 0.05).
conclusionsIn settings where not all guideline-recommended parameters are available, the DL model may improve prognostic and diagnostic performance compared with both guideline versions. Nevertheless, these exploratory findings require further validation to confirm that the observed advantages persist when all guideline-recommended parameters are systematically acquired.
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.