Abstract
Intravascular ultrasound (IVUS) is an imaging technique that allows interventional cardiologists (ICs) to capture and visually display images of blood vessels during Percutaneous Coronary Interventions (PCI). IVUS images allow ICs to: (i) evaluate the type and characteristics of lesions present, (ii) assess the appropriate intervention, (iii) determine the size of the stent or balloon to be used if they were needed, and (iv) examine if the intervention was successful. The size of the stent or balloon is determined by identifying, and then measuring, the diameters of vessel or lumen borders at the treatment locations. Identification of vessel borders is a manual time-consuming process that is rigorous to accomplish. Alternatively, a machine learning model that automatically identifies and measures the lumen and vessel borders of IVUS images was developed. This industry case study shows how the HFE team lead the UI design process to incorporate this automation into the existing UI.
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