Artificial intelligence and automation in valvular heart diseases

Artificial intelligence (AI) is gradually changing every aspect of social life, and healthcare is no exception. The clinical procedures that were supposed to, and could previously only be handled by human experts can now be carried out by machines in a more accurate and efficient way. The coming era of big data and the advent of supercomputers provides great opportunities to the development of AI technology for the enhancement of diagnosis and clinical decision-making. This review provides an introduction to AI and highlights its applications in the clinical flow of diagnosing and treating valvular heart diseases (VHDs). More specifically, this review first introduces some key concepts and subareas in AI. Secondly, it discusses the application of AI in heart sound auscultation and medical image analysis for assistance in diagnosing VHDs. Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery. This review also describes the state-of-the-art autonomous surgical robots and their roles in cardiac surgery and intervention

Automated detection of aortic annulus sizing based on decision level fusion

Aortic valve disease occurs due to calcification on the area of leaflets and it is progressive over time. Surgical Aortic Valve Replacement (SAVR) can be performed to treat the patient. However, due to invasive procedure of SAVR, a new method known as Transcatheter Aortic Valve Implantation (TAVI) has been introduced, where a synthetic catheter is placed within the patient’s heart valve. Traditionally, aortic annulus sizing procedure requires manual measurement of scanned images acquired from different imaging modalities which are Computed Tomographic (CT) and echocardiogram where both of the modalities produce inconsistency in measuring the aortic annulus yet able to produce different parameters which lead to accurate measurement. In this research, the image processing techniques of CT scan and echocardiogram images are done separately in order to obtain the aortic annulus size. Intensity adjustment and median filter are applied to CT scan image pre-processing, Watershed Transformation associated with the morphological operation has been used to perform the aortic annulus segmentation while image resizing and wavelet denoising method have been performed in echocardiogram image pre-processing followed by the implementation of Otsu N-clustering and morphological operation method for object segmentation. Then, Euclidean distance formula is applied to measure the distance between two points that indicates the diameter of the aortic annulus. Finally, a decision fusion technique based on the mathematical statistic approach has been applied to fuse the measured annulus size obtained from both modalities. Results affirmed the approach’s ability to achieve accurate annulus measurements when the final results are compared with the ground truth. In addition, the application of non-probabilistic estimation on the decision level fusion approach which does not required the dataset training produces fast computational time and helps in determining the optimal size of new aortic valve to be implemented in human heart

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area

Diagnosis and management of left valvular heart disease with advanced echocardiography and cardiac computed tomography

This thesis explored the diagnosis, management and prognosis of the most common valvular heart diseases: aortic stenosis (AS) and mitral regurgitation (MR) and enlightened their challenging types: the discordant low-gradient severe AS and the secondary MR in non-ischemic cardiomyopathy. This thesis provides new insights into the use of fusion aortic valve area index, by  incorporating the measurement of left ventricular outflow tract area on cardiac computed tomography in the continuity equation, for the diagnosis of low-gradient AS. For the treatment of low-gradient AS, TAVR is shown to result in reverse LV remodeling and functional recovery. In comparison to other minimal invasive surgical methods it results in less prosthesis-patient-mismatch although paravalvular aortic regurgitation is a caveat. Regarding the diagnostic assessment of secondary MR due to LV dysfunction this thesis concluded that LV GLS reflects the real LV dysfunction while LVEF overestimates LV function without accounting for the forward LV flow. Mitral valve repair offers LV reverse remodeling and increase in forward flow when used for the treatment of this challenging condition. Regarding the prognostication of low-gradient AS and secondary MR this thesis advocates for the evaluation of the valvular calcium on cardiac computed tomography and the evaluation of LV GLS and forward flow that are associated with survival.LUMC / Geneeskund

The mitral valve computational anatomy and geometry analysis

We present a novel methodology to characterize and quantify the Mitral Valve (MV) geometry and physical attributes in a multi-resolution framework. A multi-scale decomposition was implemented to model the MV geometry by using superquadric shape primitives and spectral reconstruction of the finer-scale geometric details. Superquadrics provide a basis to normalize the size and approximate a basic model of the MV geometry. The point-wise difference between the original geometry and the superquadric model denotes the finer-scale geometric details, which can be modeled as a scalar attribute for the MV model development. The additive decomposition of the basic MV geometry from geometric details (attributes) allows recovering the actual geometry by superposition of the superquadric approximation and the finer-details model. We implemented a lasso optimization algorithm to perform spectral analysis and develop the Fourier reconstruction of the geometric details. The spectral modeling enabled us to resample the geometric details or use spectral filters in order to adjust the spatial resolution in the model reconstruction. It also provides the basis to control the level of detail in the final model reconstruction by applying low-pass filters in the frequency domain. The higher-order attributes such as internal fiber architecture can be integrated with the geometric models using the same framework. We applied our pipeline to create models of three ovine MVs based on computed-tomography 3D images with micrometer resolution. We were able to quantify the MV leaflet geometry, reconstruct models with custom level of geometric details, and develop medial representation of the MV leaflet structure. The results show that our methodology for geometry analysis provides a basis for assessing patient-specific geometries and facilitates developing population-averaged models. Ultimately, this approach allows building personalized image-based computational models for medical device design and surgical treatment simulations.Mechanical Engineerin

Hemodynamics of Native and Bioprosthetic Aortic Valves: Insights from a Reduced Degree-of-Freedom Model

Heart disease is the leading cause of deaths in the US with aortic valve (AV) diseases being major contributors. Valve replacement is the primary therapeutic indication for AV diseases and transcatheter aortic valve replacement (TAVR) provides a safe and minimally invasive option. However, post-TAVR patient outcomes show considerable variability with deployment parameters. TAVR valves are also susceptible to failure mechanisms like leaflet thrombosis which increase the risk for serious thromboembolic events. Early detection and intervention can avert such outcomes, but symptoms often manifest at advanced stages of valve failure. Continuous monitoring can facilitate early detection, but regulatory and technological challenges may hinder developing such technology through experimental or clinical means. Computer simulations enable unprecedented predictive capabilities which can help gain insights into the pathophysiology of valvular diseases, conduct in silico trials to design novel monitoring technologies and even guide surgeries for optimal valve deployment. However, accurate, yet efficient numerical models are required. This study describes the implementation of a versatile, efficient AV dynamics model in a previously developed fluid-structure interaction solver, and its application to each of these tasks. The model accelerates simulations by simplifying the constitutive parameter space and equations governing leaflet motion without compromising accuracy. It can simulate native and prosthetic valve dynamics exhibiting physiological and pathological function in idealized and personalized aorta anatomies. This computational framework is used to generate canonical and patient-specific simulation datasets describing hemodynamic differences secondary to healthy and pathological AVs. These differences help identify biomarkers which reliably predict the risk of valvular and vascular diseases. Changes in these biomarkers are used to assess whether TAVR can deter aortic disease progression. Next, statistical differences in such biomarkers recorded by virtual wearable or embedded sensor systems, between normal and abnormal AV function, are analyzed using data-driven methods to infer valve health. This lays the groundwork for inexpensive, at-home diagnostic technologies, based on digital auscultation and in situ embedded-sensor platforms. Finally, a simulation describing the deployment of a commercially available TAVR valve in a patient-specific aorta anatomy and the associated hemodynamics is presented. Such simulations empower clinicians to optimize TAVR deployment and, consequently, patient outcomes

CURRENT CHALLENGES IN ATRIAL FIBRILLATION ABLATION

Full version unavailable due to 3rd party copyright restrictions.The ablative management of atrial fibrillation, despite a number of landmark discoveries, remains one of the most challenging fields in interventional electrophysiology. It is generally accepted that successful isolation of the pulmonary veins is a highly effective way of managing paroxysmal forms of AF. However, despite almost a decade of research into alternative lesion patterns, the solution to persistent AF remains beyond our grasp. A variety of strategies have been proposed to target key areas in the atria; these use various complex mapping systems, usually based on tailored lesion sets to try and improve outcomes. None have proven to be the golden bullet. We have investigated the role of a lesion set intended to alter the electrical properties of the posterior wall of the left atrium. Commonly known as the ‘box-set’, this pattern has shown promise in early studies and may provide some key insights into future developments. Surgical ablation using the Epicor system aims to deliver the box-set lesion, outcomes have previously been documented but each series has its limitations. In our series, very late outcomes are reported to show an 80% freedom from AF rate in patients with paroxysmal AF pre-operatively and only 20% in those with long-standing persistent forms. The reason behind this dramatic variation is explored through the invasive electrophysiologal assessment of both successful and unsuccessful cases. We report a clear correlation between the successful isolation of the posterior wall and long-term freedom from AF. Though surgical ablation may be an acceptable approach for some, the ultimate goal is a lesion set that can be delivered purely endocardially. We explore the outcome of one such empirical pattern based on the box-set concept delivered through linear catheter technology and report outcomes broadly similar to alternative patterns