Enhanced prioritization and reporting for coronary artery disease diagnosis

Abstract

Coronary artery disease (CAD) remains a significant cause of mortality, particularly in industrialized nations, contributing to a significant portion of deaths worldwide. Timely diagnosis and treatment are critical for mitigating its high morbidity and mortality rates. However, current diagnostic protocols in hospitals often suffer from inefficiencies and lengthy processes. This Bachelor’s thesis aimed to optimize the CAD diagnosis workflow within the radiology department of Hospital de la Santa Creu i Sant Pau. The focus was on implementing methodologies for automatic case reporting and patient prioritization, specifically in the scheduling of coronary computed tomography angiography (CCTA) acquisitions and determining the order of CCTA reporting. This optimization was achieved in collaboration with clinical and technological experts from the institution. The implemented algorithms are rule-based, guided by clinical criteria from the literature and physicians. The prioritization of patients ensures that severe cases receive prompt attention, aiming to reduce diagnosis time. Additionally, the automatic generation of preliminary reports integrates clinical information and findings extracted from CCTA images through image analysis. Implementing the optimized workflow for CAD diagnosis through simulation experiments demonstrated a potential reduction in diagnosis time for more severe cases and increased clinician satisfaction with the CAD diagnosis workflow and workload. This study highlights the transformative potential of automatic approaches in streamlining CAD diagnosis, offering promise for improved patient care outcomes in clinical practice.