Diagnosis and Management of Aortic Valve Stenosis: The Role of Non-Invasive Imaging

Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level

Goal: To evaluate the suitability of seismocardiogram (SCG) and gyrocardiogram (GCG) recorded at the skin level to classify aortic stenosis (AS) patients from healthy volunteers, and to determine the optimal sensor position for the classification. Methods: SCG and GCG were recorded along three axes at five chest locations of fifteen healthy subjects and AS patients. Signal frames underwent feature extraction in frequency and time-frequency domains. Then, binary classification was performed through three machine learning and three deep learning methods, considering SCG, GCG, and their combination. Results: The highest classification accuracies were achieved using Support Vector Machine (SVM) classifier, with the best sensor locations being at the mitral valve for SCG signals (92.3% accuracy) and at the pulmonary valve for GCG (92.1%). Combining SCG and GCG data allows for further improvement in the achievable accuracy (93.5%). Jointly exploiting SCG and GCG signals and both SVM- and ResNet18-based classifiers, 40 s of monitoring allows for reaching 97.2% accuracy with a single sensor on the pulmonary valve. Conclusions: Combining SCG and GCG with adequate machine learning and deep learning classifiers allows reliable classification of AS patients.

Advanced Cardiac Imaging in the Assessment of Aortic Stenosis

Aortic stenosis is the most common form of valve disease in the Western world and a major healthcare burden. Although echocardiography remains the central modality for the diagnosis and assessment of aortic stenosis, recently, advanced cardiac imaging with cardiovascular magnetic resonance, computed tomography, and positron emission tomography have provided invaluable pathological insights that may guide the personalized management of the disease. In this review, we discuss applications of these novel non-invasive imaging modalities for establishing the diagnosis, monitoring disease progression, and eventually planning the invasive treatment of aortic stenosis.

ANMCO position paper: 2022 focused update of appropriate use criteria for multimodality imaging: aortic valve disease

This document addresses the evaluation of the Appropriate Use Criteria (AUC) of multimodality imaging in the diagnosis and management of aortic valve disease. The goal of this AUC document is to provide a comprehensive resource for multimodality imaging in the context of aortic valve disease, encompassing multiple imaging modalities. Clinical scenarios are developed in a simple way to illustrate patient presentations encountered in everyday practice.