I-Corps: A Clinical Decision Support Tool to Manage Abdominal Aortic Aneurysm Patients

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is the development of a methodology that relies on recent advancements in machine learning, which have yielded positive results in diagnosis and prognosis of diseases in patients. There are 6.7 million cerebral aneurysm patients and 2.5 million abdominal aortic aneurysm patients in the United States. Patients that suffer from aneurysms are likened to having a ticking timebomb that can set off at any second, causing death or morbidity due to excessive internal bleeding. Biomechanical and shape analyses from medical images can provide greater insight into how aneurysms progress with time. However, analyses of medical images are a significant bottleneck to a clinical workflow, reducing the translation of such tools into a clinical setting. The proposed technology can reduce the overall time to extract critical information from medical images, alleviating the need for computational experts to provide an artificial intelligence-based approach to striate patient risks. The ability to personalize medicine from various indices allows clinicians to import information or data points from stress and shape analyses that are typically inaccessible to non-experts. This I-Corps project is based on the development of an aneurysm prognosis classifier that will be trained on small aneurysms, creating a robust risk score for clinicians to decide on costly surgical interventions. The approach will track changes to determine the shape of the aneurysm and biomechanical indices from various scans for a single patient at different time points, which will aid in determining the evolution of the aneurysm with time. A clinician would be able to decide on surgical intervention risk based on various factors rather than relying on the maximum diameter criterion and would be able to personalize patient healthcare management by reducing the surveillance intervals (whereby reducing overall costs of monitoring). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.