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

Santangelo, Gloria; Rossi, Andrea; Toriello, Filippo; Badano, Luigi P.; Zeitoun, David Messika; Faggiano, Pompilio

doi:10.3390/jcm10163745

Cited by 5 publications

(2 citation statements)

References 63 publications

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“…"# ≤ 4.0 m/s, 20≤ Δ𝑃 ≤40), or severe (AVA<1.0 cm 2 , AVA/BSA<0.6 cm 2 /m 2 , 𝑉 ! "# >4.0 m/s, Δ𝑃>40) [35].…”

Section: B Experimental Set-up and Protocolmentioning

confidence: 99%

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

Romano,

Maiorana,

Nusca

et al. 2024

IEEE Open J. Eng. Med. Biol.

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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.

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“…"# ≤ 4.0 m/s, 20≤ Δ𝑃 ≤40), or severe (AVA<1.0 cm 2 , AVA/BSA<0.6 cm 2 /m 2 , 𝑉 ! "# >4.0 m/s, Δ𝑃>40) [35].…”

Section: B Experimental Set-up and Protocolmentioning

confidence: 99%

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

Romano,

Maiorana,

Nusca

et al. 2024

IEEE Open J. Eng. Med. Biol.

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“…Cardiac imaging plays a key role in the assessment of AS [8,9]. Echocardiography is the gold standard for evaluating the transvalvular flow and is used to grade the degree 2 of 14 of stenosis (mild/moderate/severe).…”

Section: Introductionmentioning

confidence: 99%

Advanced Cardiac Imaging in the Assessment of Aortic Stenosis

Grodecki,

Warniello,

Spiewak

et al. 2023

JCDD

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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.

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