I-Corps: Automatic Aortic Aneurysm Screening using Deep-Learning Models

Rowan University, Glassboro NJ

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is the development of a software platform for radiologists and vascular surgeons to help reduce the time required for screening aortic aneurysms. The proposed technology is designed to improve the speed and efficiency in computed tomography (CT) scan readings, three-dimensional (3D) image reconstruction, and aortic aneurysm screening. Currently, annotating and measuring an aortic aneurysm is done manually and is time consuming. The proposed technology’s automatic screening feature may increase the productivity of doctors who routinely read the scans and improve patient experiences. In addition to aortic aneurysms, the automatic screening feature may be used in other applications in the medical screening field including brain tumors, pulmonary embolisms, and lung cancer. The results and resources developed from this project also may be used for educational purposes, where new curricula will be developed based on this project to cover the fields of neural network applications, three-dimensional (3D) image reconstruction, and image processing algorithms. This I-Corps project is based on the development of a software platform that automates the readings of computed tomography (CT) scans using algorithms to train deep-learning models and allow for efficient and accurate diameter measurement of aortic aneurysms. In particular, the proposed technology uses adaptive image processing methods based on brightness and contrast to fine tune deep-learning models and employ ensemble-based state-of-the-art objective detection models. The proposed technology may render 3D visualizations of the aorta, which may help doctors to perform more comprehensive diagnosis of the aneurysm. Moreover, based on the measurements and patient health medical record data, the proposed method is designed to calculate estimated risk of adverse aortic events (dissection, rupture, and death), and report risk-rankings to help prioritize patient treatment and surgical procedures. 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.