ABSTRACT
Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.
ABSTRACT
BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a genetic disease associated with risk of morbidity and sudden cardiac death. The prevalence, hypertrophy patterns, mode of presentations, and different ECG findings vary in different regions of the world. To date, no data is present regarding these variables in Qatar. PATIENTS AND METHODS: A retrospective, cross sectional, descriptive analysis of all patients referred for echocardiography study at Hamad General Hospital, Qatar. The study period was from January 2008 till December 2010. AIMS: To study 1) the prevalence of HCM, 2) the different patterns of hypertrophy, and 3) the clinical and ECG presentations in this population. RESULTS: Out of the 29,286 cases evaluated, 38 patients were found to have HCM (0.13%). Their clinical, ECG, and echocardiography findings were analyzed. Mean age was 47 y, 35 males (92%) and 3 females (8%). Four patterns of hypertrophy were described; 17 (44.7%) had septal hypertrophy alone, 6 (15.8%) had septal and other segments hypertrophy but sparing the apex, 10 (26.3%) had apical segments along with any other segment hypertrophy, and 5 (13.2%) had apical hypertrophy alone. No obstruction was found in 19 (50%), left ventricular outflow (LVO) tract obstruction was found in 13 (34%), and mid cavity obstruction (MCO) in 6 (16%). Twenty one (55.3%) patients were referred because of chest pain, 15 (39.5%) with palpitations, 15 (39.5%) with shortness of breath, and 5 (13.2%) with syncope. Nine patients (23.7%) were asymptomatic and were referred because of cardiac murmur during routine examination. ECG evidence of LV hypertrophy was found in 29 (76.3%). CONCLUSION: The prevalence of HCM in our population group is 0.13% with a male predominance (12:1). There was a diversity of clinical presentation, ECG abnormalities and patterns of LV hypertrophy among HCM patients.
ABSTRACT
LVAD = Left Ventricular Assist Device; LV = Left Ventricle; LA = Left Atrium; AO = Aorta.
