Abstract Body (Do not enter title and authors here): Introduction: Following trans-catheter aortic valve replacement (TAVR), patients may develop aortic valve leaflet thrombosis, a condition associated with an increased risk of embolic events. Currently, the diagnosis of post-TAVR leaflet thrombosis is based on echocardiography surveillance or cardiac tomography (CCT) scan, both require specialized team and equipment, and may also expose the patients to radiation and contrast.
Acoustic analysis is a non-invasive reproducible method, which incorporates the recording of cardiac sounds and analyzing them via machine learning algorithms.
Here, we investigated the diagnostic abilities of acoustic analysis in the detection of post-TAVR leaflet thrombosis.

Methods: Using an electronic stethoscope, heart sounds were prospectively recorded at 2 time points: 24-hours and 6-months post-TAVR. CCT scan was performed 6-months following TAVR to detect leaflet thrombosis. Acoustic sounds were analyzed, using a Gaussian support vector machine (SVM) algorithm, to identify features associated with leaflet thrombosis.
Results: Of 19 consecutive patients, 3 were found to have high-grade leaflet thrombosis based on CCT results. The SVM algorithm effectively classified heart sounds into "normal" and "thrombotic", with a sensitivity of 100% and specificity of 97.2%. Following anticoagulation therapy, all 3 patients showed no signs of leaflet thrombosis on follow-up CCT scans and were also classified as "normal" by the algorithm. Notably, at the time of leaflet thrombosis, Doppler echocardiographies showed normal pressure gradients across the affected valves.
Conclusions: These initial results suggest that acoustic analysis, combined with machine learning algorithms, may identify post-TAVR leaflet thrombosis, providing a non-invasive, cost-effective, and radiation-free alternative to traditional diagnostic methods.