  In this paper, we explored the use of deep learning for the prediction of
aortic flow metrics obtained using 4D flow MRI using wearable
seismocardiography (SCG) devices. 4D flow MRI provides a comprehensive
assessment of cardiovascular hemodynamics, but it is costly and time-consuming.
We hypothesized that deep learning could be used to identify pathological
changes in blood flow, such as elevated peak systolic velocity Vmax in patients
with heart valve diseases, from SCG signals. We also investigated the ability
of this deep learning technique to differentiate between patients diagnosed
with aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve
(BAV), non-AS patients with a mechanical aortic valve (MAV), and healthy
subjects with a normal tricuspid aortic valve (TAV). In a study of 77 subjects
who underwent same-day 4D flow MRI and SCG, we found that the Vmax values
obtained using deep learning and SCGs were in good agreement with those
obtained by 4D flow MRI. Additionally, subjects with TAV, BAV, MAV, and AS
could be classified with ROC-AUC values of 92%, 95%, 81%, and 83%,
respectively. This suggests that SCG obtained using low-cost wearable
electronics may be used as a supplement to 4D flow MRI exams or as a screening
tool for aortic valve disease.
