UC Berkeley

Machine learning with deep neural networks has demonstrated high performance for high dimensionality prediction tasks across multiple domains with sufficient sample data. Cardiovascular disease is a pertinent public health issue that has the potential to be better understood and addressed via deep learning approaches. In this work, we study machine learning approaches toward diagnosing various forms of cardiovascular disease and predicting its biomechanical behavior across multiple scales.

We begin by training deep learning models for an initial classification objective in echocardiography, a ubiquitous imaging modality for cardiologists. For view classification, we are able to demonstrate physician-level performance. We then expand the work from a methods and clinical application perspective. We address the high cost of annotation in medical imaging by examining data-efficient supervised and semi-supervised algorithms. In addition, we expand our prediction tasks towards the ultimate goal of automated, accurate cardiovascular disease diagnosis by predicting left ventricular hypertrophy.

To understand the nature of cardiovascular disease and develop treatments, a close look at the underlying biomechanics is important. For atherosclerosis, a leading cause of morbidity and mortality, we bridge finite element methods and machine learning to predict arterial tissue stress. Likewise for cytoskeletal proteins, which are the structural building blocks of human biology and influence cardiovascular health, we develop graph neural network algorithms to predict force response and conformational dynamics in calponin homology domains.

Moreover, we hope to lay the groundwork to advance the intersection of machine learning, biomechanics, and cardiovascular disease.