Predicting pressure gradient using artificial intelligence for transcatheter aortic valve replacement.

Objective: After transcatheter aortic valve replacement, the mean transvalvular pressure gradient indicates the effectiveness of the therapy. The objective is to develop artificial intelligence to predict the post-transcatheter aortic valve replacement aortic valve pressure gradient and aortic valve area from preprocedural echocardiography and computed tomography data. Methods: A retrospective study was conducted on patients who underwent transcatheter aortic valve replacement due to aortic valve stenosis. A total of 1091 patients were analyzed for pressure gradient predictions (mean age 76.8 ± 9.2 years, 57.8% male), and 1063 patients were analyzed for aortic valve area predictions (mean age 76.7 ± 9.3 years, 57.2% male). An artificial intelligence learning model was trained (training: n = 663 patients, validation: n = 206 patients) and tested (testing: n = 222 patients) to predict pressure gradient, and a separate artificial intelligence learning model was trained (training: n = 640 patients, validation: n = 218 patients) and tested (testing: n = 205 patients) for predicting aortic valve area. Results: The mean absolute error for pressure gradient and aortic valve area predictions was 3.0 mm Hg and 0.45 cm Conclusions: The artificial intelligence-based algorithm has demonstrated potential in predicting post-transcatheter aortic valve replacement transvalvular pressure gradient predictions for patients with aortic valve stenosis. Further studies are necessary to differentiate pressure gradient between valve types.