ArticleJournal of echocardiography2025
Novel deep learning framework for simultaneous assessment of left ventricular mass and longitudinal strain: clinical feasibility and validation in patients with hypertrophic cardiomyopathy.
Article in Journal of echocardiography, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Fully automated artificial intelligence-based echocardiographic analysis substantially reduces workflow time while preserving measurement accuracy: a pilot study.Journal of cardiovascular imaging · 2026Article
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Deep learning-based multi-view echocardiographic framework for comprehensive diagnosis of pericardial disease.European heart journal. Digital health · 2026Article
- Machine-learning-derived phenotypes of hypertensive patients using multidimensional clinical and echocardiographic data including strain imaging.European heart journal. Digital health · 2026Article
- Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2026Review
- Cardiac magnetic resonance research advances in myocardial fibrosis of hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundThis study aims to present the Segmentation-based Myocardial Advanced Refinement Tracking (SMART) system, a novel artificial intelligence (AI)-based framework for transthoracic echocardiography (TTE) that incorporates motion tracking and left ventricular (LV) myocardial segmentation for automated LV mass (LVM) and global longitudinal strain (LVGLS) assessment.
methodsThe SMART system demonstrates LV speckle tracking based on motion vector estimation, refined by structural information using endocardial and epicardial segmentation throughout the cardiac cycle. This approach enables automated measurement of LVM
resultsLVGLS
conclusionsThe SMART technique provides a comparable LVGLS evaluation and a more accurate LVM assessment than conventional TTE, with predictive values for myocardial fibrosis and adverse outcomes. These findings support its utility in HCM management.
Indexed as
Identifiers
40650815What 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.