ArticleJournal of echocardiography2026
Validation of a fully automated workflow for longitudinal strain using artificial intelligence.
Article in Journal of echocardiography, 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
Abstract
backgroundManual interpretation of echocardiographic data for strain analysis is time-consuming and prone to inter-observer variability. With the advent of artificial intelligence (AI), its potential to streamline echocardiographic interpretation and reduce variability has attracted increasing interest. This study aimed to evaluate the accuracy of AI-derived strain measurements, focusing on left ventricular global longitudinal strain (LVGLS) and left atrial reservoir strain (LASr), compared with expert sonographer measurements. METHODS AND
resultsA total of 150 patients were included regardless of underlying disease. Echocardiographic parameters, including LVGLS and LASr, were independently assessed by an expert sonographer and the fully automated software (Us2.ai). Correlations and agreement were evaluated using intraclass correlation coefficients (ICC) and complementary analyses. Subgroup analyses were also performed for LVGLS stratified by left ventricular ejection fraction (EF) categories, and for LASr stratified by left atrial volume index (LAVi) categories. For LVGLS, the ICC was 0.83 (95% CI: 0.77-0.88) and 0.77 (95% CI: 0.67-0.84) for LASr. The accuracy of both LVGLS and LASr was generally maintained across different EF and LAVi subcategories.
conclusionsThe fully automated software showed reliable correlations with expert sonographer measurements, particularly for LVGLS, while correlation for LASr was not as high. With further investigation, the fully automated software may become a useful tool for echocardiographic analysis in clinical practice.
Indexed as
Identifiers
42166090What 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.