Explainable AI for cardiac monitoring

Abstract

This thesis explored the integration of AI, and particularly deep learning, into various stages of the myocardial infarction (MI) clinical workflow, ranging from imaging and physiological monitoring to the extraction of structured data from free-text reports. Across different modalities and tasks, we investigated how AI and, if relevant, explainable AI methods can reduce clinical workload.

In Chapter 2, we addressed the growing interest in deep learning–based analysis of invasive coronary angiography (ICA) by conducting a comprehensive systematic review.

In Chapter 3, we investigated how LLMs can automate the extraction of clinically relevant variables from semi-structured ICA and echocardiography reports.

In Chapter 4, 5 and 6, we focused on ECG as a widely accessible, non-invasive signal for cardiac assessment. We developed and evaluated explainable AI models based on VAEs, aimed at predicting LVF, a critical post-MI prognostic marker, and mortality. Taken together, the studies in this thesis illustrate how deep learning models, when carefully tailored to clinical tasks, can contribute to a more efficient and scalable MI workflow. From automating complex imaging analyses to structuring textual data and deriving interpretable physiological markers, AI offers tangible solutions to alleviate the growing burden on healthcare systems.