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In this paper, we explored the use of deep learning for the prediction ofaortic flow metrics obtained using 4D flow MRI using wearableseismocardiography (SCG) devices. 4D flow MRI provides a comprehensiveassessment of cardiovascular hemodynamics, but it is costly and time-consuming.We hypothesized that deep learning could be used to identify pathologicalchanges in blood flow, such as elevated peak systolic velocity Vmax in patientswith heart valve diseases, from SCG signals. We also investigated the abilityof this deep learning technique to differentiate between patients diagnosedwith aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve(BAV), non-AS patients with a mechanical aortic valve (MAV), and healthysubjects with a normal tricuspid aortic valve (TAV). In a study of 77 subjectswho underwent same-day 4D flow MRI and SCG, we found that the Vmax valuesobtained using deep learning and SCGs were in good agreement with thoseobtained by 4D flow MRI. Additionally, subjects with TAV, BAV, MAV, and AScould be classified with ROC-AUC values of 92%, 95%, 81%, and 83%,respectively. This suggests that SCG obtained using low-cost wearableelectronics may be used as a supplement to 4D flow MRI exams or as a screeningtool for aortic valve disease.

A deep learning approach to using wearable seismocardiography (SCG) for  diagnosing aortic valve stenosis and predicting aortic hemodynamics obtained  by 4D flow MRI