Although cardiac amyloidosis and hypertrophic cardiomyopathy (HCM) are relatively rare causes of heart failure (HF), they are particularly challenging to detect and treat for several shared reasons: (1) on routine clinical imaging (i.e., echocardiography [echo]), they can be difficult to distinguish from superficially similar, more common forms of cardiac disease that cause left ventricular (LV) hypertrophy; (2) the diagnoses are often missed and thus patients can present late in the course of disease at a time when treatment is difficult; (3) objective, noninvasive metrics that reliably reflect disease progression have not been identified; and (4) the small number of known patients with these diseases can make epidemiology studies and clinical trials difficult to organize and conduct. For both cardiac amyloidosis and HCM, echo plays a critical role in both diagnosis and longitudinal monitoring given its ubiquitous clinical availability, safety, and low cost. More broadly, echo dominates the current landscape of routine cardiac imaging, with tens of millions of echos performed in the United States each year. However, the clinical challenges described above highlight several shortcomings of echo: it is limited in its ability to (1) diagnose disease at its early stages; (2) discriminate between morphologically similar diseases; and (3) quantify disease progression. This proposal seeks to address deficiencies in the current echo reading workflow, which is subjective and captures only a small fraction of the data available in each study. The overall objective of this application is to use advances in machine learning to develop and validate fully-automated echo image analytic approaches to diagnose and track rare cardiomyopathies, focusing on cardiac amyloidosis and HCM. Our proposal is centered on the hypothesis that highly scalable computer vision methods can be applied to echo studies to overcome limitations of the standard clinical echo reading workflow. Accordingly our aims are: (1) Apply an automated method for echo quantification and disease identification to detect and differentiate cardiac diseases that cause increased LV wall thickness; and (2) Characterize quantifiable echo measures of disease progression in cardiac amyloidosis and HCM and associate these with clinical outcomes. Our multidisciplinary team, which is composed of experts in cardiomyopathies, echocardiography, computer vision, and machine learning, will analyze echos and patient data from 2 large patient registries: the Multicenter Amyloid Phenotyping Study (MAPS) and the Sarcomeric Human Cardiomyopathy Registry (SHaRe) HCM Network, with validation using a repository of nearly 400,000 echos. The successful completion of our aims will result in an innovative tool for early diagnosis of myocardial diseases and tracking of disease progression. Importantly, our project will set the stage for conducting larger epidemiology studies of rare myocardial diseases by automating the identification of these patients, and thereby developing previously unattainable broad-based cohorts for these conditions.
Rare heart diseases such as amyloidosis (due to abnormal deposition of a protein into the heart muscle) and hypertrophic cardiomyopathy (due to a genetic mutation) are important causes of heart failure and sudden death in the general population. These heart diseases are difficult to diagnose, monitor, and treat because: (1) they appear superficially similar to more common forms of heart disease (e.g., high blood pressure) on imaging tests; (2) the optimal monitoring of disease progression over time has not been established; and (3) there is a lack of large-scale studies of patients with these diseases given their rarity. This project aims to use machine learning (artificial intelligence) of digital echocardiographic images to create a completely automated method for diagnosing and tracking these rare heart diseases with the ultimate goal of broad deployment of artificial intelligence algorithms in hospitals and clinics to help identify patients with these rare heart diseases earlier, better predict adverse outcomes, and increase the size and scope of patient registries to enhance research of these conditions.