The Journal of thoracic and cardiovascular surgery · 2025
Machine learning methods to predict transvalvular gradient waveform post-transcatheter aortic valve replacement using preprocedural echocardiogram.
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Song Wenyuan, Sirset-Becker Taylor, Mata Quinonez Luis René, Polsani Dhruv, Polsani Venkateshwar, Yadav Pradeep, Thourani Vinod, Dasi Lakshmi Prasad
Аннотация
OBJECTIVE: 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 preprocedural Doppler echocardiogram.
METHODS: A 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.
RESULTS: 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 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.
CONCLUSIONS: A 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.
METHODS: A 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.
RESULTS: 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 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.
CONCLUSIONS: A 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.