Vi utbildar för framtiden och skapar samhällsnytta genom vår forskning som levandegörs i nära samarbete med näringslivet. Vi bedriver forskning inom computer science, datateknik, software engineering och interaktionsdesign - från grundforskning till direkta tillämpningar. Institutionen har en stark internationell prägel och är delad mellan Chalmers och Göteborgs universitet.
We are engaged in research and education across the full spectrum of computer science, computer engineering, software engineering, and interaction design, from foundations to applications. We educate for the future, conduct research with high international visibility, and create societal benefits through close cooperation with businesses and industry. The department is joint between Chalmers and the University of Gothenburg.
(2019) FINATI, SARA; LJUNGGREN, ELIN; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Torkar, Richard; Aguirre Nilsson, Charlotta; Velic, Medina
Abdominal Aortic Aneurysm (AAA) is a localized enlargement of the abdominal
aorta that can progress to a rupture, which will cause an internal bleeding that is
fatal in the majority of the cases. To save more lives, a nationwide screening program
invites all men at 65 to measure their largest aortic diameter during an ultrasound
examination. The diagnosis is based solely on this diameter and if it is below 30
mm the patient is declared healthy and discarded from any follow-up monitoring.
However, recent studies have shown that patients with a diameter within 25–29 mm,
a sub-aneurysm, run an elevated risk of developing a full-size aneurysm and hence
might need further surveillance.
This thesis is a collaboration between the product development company QRTECH
AB and Västra Götalandsregionen (VGR). It proposes a novel solution for predicting
which sub-aneurysms that might grow into a full-size aneurysm with the help of an
ultrasound image complemented by patient data including aortic diameter, number
of years of smoking and snus, blood pressure and medications. The solution consists
of a multimodal deep learning algorithm that classifies the sub-aneurysms as either
healthy or sick and thereby suggests patients that should be kept under surveillance.
Due to lack of any follow-up data for the men with sub-aneurysms a comparison
with meta-studies, examining how many sub-aneurysms that progressed into a fullsize
aneurysm, was carried out. The results from those studies did not agree with
the results obtained from classifying the sub-group in this project. A feasible explanation
is the limited data set which most likely affected the learning. However, the
evaluation of the model’s performance was still promising and indicates the potential
of using neural networks for diagnosing AAA.