ArticleNPJ digital medicine2025
Artificial intelligence prediction of age from echocardiography as a marker for cardiovascular disease.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Cardiac and systemic biological age capture complementary aging signals associated with cardiovascular disease.Frontiers in cardiovascular medicine · 2026Article
- Predicted 10-year cardiometabolic risk among hospital staff: a cross-sectional study.Frontiers in public health · 2026Article
Corrections and comments
- Update of
Authors and funding
15 authors.
Funding
Abstract
While chronological age is a universal risk predictor across most populations and diseases, distinguishing between biologically older from younger individuals may identify individuals with accelerated or delayed cardiovascular aging. This study presents a deep learning model to predict age from echocardiogram videos, leveraging 2,610,266 videos from 166,508 studies from 90,738 patients. Leveraging multi-view echocardiography, our model achieved a mean absolute error (MAE) of 6.76 (6.65-6.87) years and a coefficient of determination (R
Identifiers
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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.