Machine learning for diagnosing functional heart disease in echocardiography

Heart failure (HF) is associated with poor patient outcomes and burdens healthcare systems and clinicians. Fortunately, therapeutic options are available for managing cardiac dysfunction if diagnosed early. Echocardiography (echo) can be used to assess cardiac function swiftly and detect signs or risk factors of HF. Nonetheless, echo acquisition and interpretation require extensive training and experience, leading to exceeding demand for the available clinical echo services. This thesis investigates the feasibility of machine learning (ML)-based solutions for analyzing heart function based on clinical echo data and available annotations. The goal is to automate measurements of indicators of functional diseases. We focus on guideline-aware supervised learning frameworks for assessing LV ejection fraction (EF), regional wall motion abnormality (WMA), and LV diastolic dysfunction (LVDD). We propose spatio-temporal neural networks to determine EF from echo cine loops. We utilize multi-task learning with observer variability modelling is leverage the label noise and decouple errors in different available EF labels. In the context of regional systolic function, we present an error quantification and visualization framework to evaluate the generalizability of disease-agnostic models on diseased cohorts. We validate segmentation models trained on standard populations in a WMA cohort and report global and local error metrics with weak wall segment labels. This framework enables us to further identify failure modes in trained ML models. Using the errors obtained from the weak labels, we observed that segmentation performance might become jeopardized in the presence of akinetic LV wall segments. Finally, in the most extensive study of its kind, we demonstrate the impacts of the updated clinical guidelines for diastolic function assessment based on measurements derived from echo. We propose a neural network to replicate the latest clinical guidelines for diastolic function classification and extend this model to a regression framework to obtain a novel continuous LVDD scoring system. Increasing the size and diversity of the training and test set for model training and clinical validation is critical to further developing ML-driven heart disease diagnostic tools. Future work may involve ML-based multi-chamber quantification, myocardium localization, and Doppler image analysis toward automatic disease diagnosis.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat