The Journal of thoracic and cardiovascular surgeryJournal Article
02 May 2025
Time-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 pre-procedural Doppler echocardiogram.
A total of 110 patients undergoing TAVR (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 post-procedural pressure gradient waveform from pre-procedural pressure gradient waveform based on the proposed generative active learning framework.
The 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 ground truth. The generative method improved prediction accuracy by 3.11%, while the data selection strategy increased it by 16.03% compared to 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.
A deep, generative, active machine learning model was developed to output the prediction of post-TAVR time-varying pressure gradient from the pre-procedural time-varying gradient obtained from Doppler echocardiogram. Such a predictive method may help guide decision-making for the prevention of various post-TAVR complications. Further studies are necessary to investigate the gradient change of other valve types.
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