ArticleThe Journal of thoracic and cardiovascular surgery2025
Machine learning methods to predict transvalvular gradient waveform post-transcatheter aortic valve replacement using preprocedural echocardiogram.
Article in The Journal of thoracic and cardiovascular surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Deep learning for predicting transvalvular gradient outcomes for patients undergoing transcatheter aortic valve replacement for native aortic stenosis.JTCVS structural and endovascular · 2026Article
- Redefining Aortic Valve Replacement: The Transition from Open Surgery to Transcatheter Innovation.Journal of clinical medicine · 2025Review
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Authors and funding
8 authors.
Funding
Abstract
objectiveTime-varying transvalvular pressure gradient after transcatheter aortic valve replacement indicates the effectiveness of the therapy. The objective was to develop a novel machine learning method enhanced by generative artificial intelligence and smart data selection strategies to predict the post-transcatheter aortic valve replacement gradient waveform using preprocedural Doppler echocardiogram.
methodsA total of 110 patients undergoing transcatheter aortic valve replacement (mean age 78.2 ± 9.0 years, 52.5% female) were included for pressure gradient collection. A deep machine learning model was trained and tested to predict postprocedural pressure gradient waveform from preprocedural pressure gradient waveform based on the proposed generative active learning framework.
resultsThe trained model demonstrated an average prediction accuracy of 84.85% across the 10 test patients measured from the relative mean absolute error between the predicted gradient waveform and the ground truth. The generative method improved prediction accuracy by 3.11%, whereas the data selection strategy increased it by 16.03% compared with the baseline experimental group using plain machine learning. Additionally, Bland-Altman analysis demonstrated a strong agreement between the proposed method and clinical measurements for both mean and peak pressure gradient predictions.
conclusionsA deep, generative, active machine learning model was developed to output the prediction of post-transcatheter aortic valve replacement time-varying pressure gradient from the preprocedural time-varying gradient obtained from Doppler echocardiogram. Such a predictive method may help guide decision-making for the prevention of various post-transcatheter aortic valve replacement complications. Further studies are necessary to investigate the gradient change of other valve types.
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