TUBE-LOAD MODEL AS A DIGITAL TWIN FOR ABDOMINAL AORTIC ANEURYSM PATIENTS

Abstract

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by the abnormal dilation of the aorta, which can lead to vessel rupture and high mortality rates (>80%). Alarmingly, AAA is often asymptomatic and can remain undetected until it reaches a critical size or ruptures. Current methods for diagnosing and monitoring AAA, such as ultrasound, CT, and MRI, are effective but expensive for regular use and require specialized operators. These limitations hinder the widespread use of imaging-based techniques for regular AAA screening and surveillance. Therefore, creating a need for more accessible, affordable, and convenient tools to detect AAA in its early stages, monitor its progression, and assess treatment efficacy. This thesis explores the potential of tube-load (TL) model to non-invasively monitor AAA progression by analyzing arterial pressure waveforms, which change in response to aneurysm-induced alterations in aortic geometry and mechanical properties. These changes are captured and revealed by the parameters of the TL model.

To evaluate the TL model’s capability to monitor AAA, we applied it to carotid and femoral artery tonometry waveforms collected from 79 subjects, including both controls and AAA subjects, as well as a subset of 35 AAA subjects before and after endovascular repair (EVAR) surgery. Our analysis showed that the TL model could fit the waveforms from pre-EVAR AAA subjects as accurately as those from controls and post-EVAR. Moreover, the TL model parameters exhibited physiologically explainable changes consistent with the structural changes of the aorta associated with AAA and its treatment. These findings suggest that the TL model has the potential as a digital twin to enable convenient and cost-effective personalized AAA monitoring.